## Input

- Input table: Output table of SentenceExtractor Example

## SQL Call

SELECT * FROM POSTagger ( ON SentenceExtractor ( ON paragraphs_input USING TextColumn ('paratext') Accumulate ('paraid') ) USING TextColumn ('sentence') Accumulate ('sentence','sentence_sn') ) AS dt ORDER BY sentence_sn, word_sn;

## Output

sentence sentence_sn word_sn word pos_tag ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- ----------- ------- ------------------- ------- in statistics, simple linear regression is the least squares estimator of a linear regression model with a single explanatory variable. in other words, simple linear regression fits a straight line through the set of n points in such a way that makes the sum of squared residuals of the model (that is, vertical distances between the points of the data set and the fitted line) as small as possible. 1 1 in IN logistic regression was developed by statistician david cox in 1958[2][3](although much work was done in the single independent variable case almost two decades earlier). the binary logistic model is used to estimate the probability of a binary response based on one or more predictor (or independent) variables (features). as such it is not a classification method. it could be called a qualitative response/discrete choice model in the terminology of economics. 1 1 logistic JJ association rule learning is a method for discovering interesting relations between variables in large databases. it is intended to identify strong rules discovered in databases using different measures of interestingness. based on the concept of strong rules, rakesh agrawal et al.[2] introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (pos) systems in supermarkets. for example, the rule {onions, potatoes}=>{burger} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. 1 1 association NN decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the items target value. it is one of the predictive modelling approaches used in statistics, data mining and machine learning. tree models where the target variable can take a finite set of values are called classification trees. in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. 1 1 decision NN cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 1 cluster NN cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 2 analysis NN logistic regression was developed by statistician david cox in 1958[2][3](although much work was done in the single independent variable case almost two decades earlier). the binary logistic model is used to estimate the probability of a binary response based on one or more predictor (or independent) variables (features). as such it is not a classification method. it could be called a qualitative response/discrete choice model in the terminology of economics. 1 2 regression NN decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the items target value. it is one of the predictive modelling approaches used in statistics, data mining and machine learning. tree models where the target variable can take a finite set of values are called classification trees. in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. 1 2 tree NN association rule learning is a method for discovering interesting relations between variables in large databases. it is intended to identify strong rules discovered in databases using different measures of interestingness. based on the concept of strong rules, rakesh agrawal et al.[2] introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (pos) systems in supermarkets. for example, the rule {onions, potatoes}=>{burger} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. 1 2 rule NN in statistics, simple linear regression is the least squares estimator of a linear regression model with a single explanatory variable. in other words, simple linear regression fits a straight line through the set of n points in such a way that makes the sum of squared residuals of the model (that is, vertical distances between the points of the data set and the fitted line) as small as possible. 1 2 statistics NNS logistic regression was developed by statistician david cox in 1958[2][3](although much work was done in the single independent variable case almost two decades earlier). the binary logistic model is used to estimate the probability of a binary response based on one or more predictor (or independent) variables (features). as such it is not a classification method. it could be called a qualitative response/discrete choice model in the terminology of economics. 1 3 was VBD association rule learning is a method for discovering interesting relations between variables in large databases. it is intended to identify strong rules discovered in databases using different measures of interestingness. based on the concept of strong rules, rakesh agrawal et al.[2] introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (pos) systems in supermarkets. for example, the rule {onions, potatoes}=>{burger} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. 1 3 learning NN in statistics, simple linear regression is the least squares estimator of a linear regression model with a single explanatory variable. in other words, simple linear regression fits a straight line through the set of n points in such a way that makes the sum of squared residuals of the model (that is, vertical distances between the points of the data set and the fitted line) as small as possible. 1 3 , O decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the items target value. it is one of the predictive modelling approaches used in statistics, data mining and machine learning. tree models where the target variable can take a finite set of values are called classification trees. in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. 1 3 learning NN cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 3 or CC cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 4 clustering NN association rule learning is a method for discovering interesting relations between variables in large databases. it is intended to identify strong rules discovered in databases using different measures of interestingness. based on the concept of strong rules, rakesh agrawal et al.[2] introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (pos) systems in supermarkets. for example, the rule {onions, potatoes}=>{burger} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. 1 4 is VBZ logistic regression was developed by statistician david cox in 1958[2][3](although much work was done in the single independent variable case almost two decades earlier). the binary logistic model is used to estimate the probability of a binary response based on one or more predictor (or independent) variables (features). as such it is not a classification method. it could be called a qualitative response/discrete choice model in the terminology of economics. 1 4 developed VBN in statistics, simple linear regression is the least squares estimator of a linear regression model with a single explanatory variable. in other words, simple linear regression fits a straight line through the set of n points in such a way that makes the sum of squared residuals of the model (that is, vertical distances between the points of the data set and the fitted line) as small as possible. 1 4 simple JJ decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the items target value. it is one of the predictive modelling approaches used in statistics, data mining and machine learning. tree models where the target variable can take a finite set of values are called classification trees. in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. 1 4 uses VBZ decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the items target value. it is one of the predictive modelling approaches used in statistics, data mining and machine learning. tree models where the target variable can take a finite set of values are called classification trees. in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. 1 5 a DT in statistics, simple linear regression is the least squares estimator of a linear regression model with a single explanatory variable. in other words, simple linear regression fits a straight line through the set of n points in such a way that makes the sum of squared residuals of the model (that is, vertical distances between the points of the data set and the fitted line) as small as possible. 1 5 linear JJ cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 5 is VBZ logistic regression was developed by statistician david cox in 1958[2][3](although much work was done in the single independent variable case almost two decades earlier). the binary logistic model is used to estimate the probability of a binary response based on one or more predictor (or independent) variables (features). as such it is not a classification method. it could be called a qualitative response/discrete choice model in the terminology of economics. 1 5 by IN association rule learning is a method for discovering interesting relations between variables in large databases. it is intended to identify strong rules discovered in databases using different measures of interestingness. based on the concept of strong rules, rakesh agrawal et al.[2] introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (pos) systems in supermarkets. for example, the rule {onions, potatoes}=>{burger} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. 1 5 a DT decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the items target value. it is one of the predictive modelling approaches used in statistics, data mining and machine learning. tree models where the target variable can take a finite set of values are called classification trees. in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. 1 6 decision NN association rule learning is a method for discovering interesting relations between variables in large databases. it is intended to identify strong rules discovered in databases using different measures of interestingness. based on the concept of strong rules, rakesh agrawal et al.[2] introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (pos) systems in supermarkets. for example, the rule {onions, potatoes}=>{burger} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. 1 6 method NN in statistics, simple linear regression is the least squares estimator of a linear regression model with a single explanatory variable. in other words, simple linear regression fits a straight line through the set of n points in such a way that makes the sum of squared residuals of the model (that is, vertical distances between the points of the data set and the fitted line) as small as possible. 1 6 regression NN cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 6 the DT logistic regression was developed by statistician david cox in 1958[2][3](although much work was done in the single independent variable case almost two decades earlier). the binary logistic model is used to estimate the probability of a binary response based on one or more predictor (or independent) variables (features). as such it is not a classification method. it could be called a qualitative response/discrete choice model in the terminology of economics. 1 6 statistician JJ logistic regression was developed by statistician david cox in 1958[2][3](although much work was done in the single independent variable case almost two decades earlier). the binary logistic model is used to estimate the probability of a binary response based on one or more predictor (or independent) variables (features). as such it is not a classification method. it could be called a qualitative response/discrete choice model in the terminology of economics. 1 7 david JJ cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 7 task NN decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the items target value. it is one of the predictive modelling approaches used in statistics, data mining and machine learning. tree models where the target variable can take a finite set of values are called classification trees. in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. 1 7 tree NN association rule learning is a method for discovering interesting relations between variables in large databases. it is intended to identify strong rules discovered in databases using different measures of interestingness. based on the concept of strong rules, rakesh agrawal et al.[2] introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (pos) systems in supermarkets. for example, the rule {onions, potatoes}=>{burger} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. 1 7 for IN in statistics, simple linear regression is the least squares estimator of a linear regression model with a single explanatory variable. in other words, simple linear regression fits a straight line through the set of n points in such a way that makes the sum of squared residuals of the model (that is, vertical distances between the points of the data set and the fitted line) as small as possible. 1 7 is VBZ in statistics, simple linear regression is the least squares estimator of a linear regression model with a single explanatory variable. in other words, simple linear regression fits a straight line through the set of n points in such a way that makes the sum of squared residuals of the model (that is, vertical distances between the points of the data set and the fitted line) as small as possible. 1 8 the DT logistic regression was developed by statistician david cox in 1958[2][3](although much work was done in the single independent variable case almost two decades earlier). the binary logistic model is used to estimate the probability of a binary response based on one or more predictor (or independent) variables (features). as such it is not a classification method. it could be called a qualitative response/discrete choice model in the terminology of economics. 1 8 cox NN association rule learning is a method for discovering interesting relations between variables in large databases. it is intended to identify strong rules discovered in databases using different measures of interestingness. based on the concept of strong rules, rakesh agrawal et al.[2] introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (pos) systems in supermarkets. for example, the rule {onions, potatoes}=>{burger} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. 1 8 discovering VBG decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the items target value. it is one of the predictive modelling approaches used in statistics, data mining and machine learning. tree models where the target variable can take a finite set of values are called classification trees. in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. 1 8 as IN cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 8 of IN decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the items target value. it is one of the predictive modelling approaches used in statistics, data mining and machine learning. tree models where the target variable can take a finite set of values are called classification trees. in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. 1 9 a DT logistic regression was developed by statistician david cox in 1958[2][3](although much work was done in the single independent variable case almost two decades earlier). the binary logistic model is used to estimate the probability of a binary response based on one or more predictor (or independent) variables (features). as such it is not a classification method. it could be called a qualitative response/discrete choice model in the terminology of economics. 1 9 in IN cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 9 grouping VBG in statistics, simple linear regression is the least squares estimator of a linear regression model with a single explanatory variable. in other words, simple linear regression fits a straight line through the set of n points in such a way that makes the sum of squared residuals of the model (that is, vertical distances between the points of the data set and the fitted line) as small as possible. 1 9 least JJS association rule learning is a method for discovering interesting relations between variables in large databases. it is intended to identify strong rules discovered in databases using different measures of interestingness. based on the concept of strong rules, rakesh agrawal et al.[2] introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (pos) systems in supermarkets. for example, the rule {onions, potatoes}=>{burger} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. 1 9 interesting JJ cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 10 a DT in statistics, simple linear regression is the least squares estimator of a linear regression model with a single explanatory variable. in other words, simple linear regression fits a straight line through the set of n points in such a way that makes the sum of squared residuals of the model (that is, vertical distances between the points of the data set and the fitted line) as small as possible. 1 10 squares VBZ association rule learning is a method for discovering interesting relations between variables in large databases. it is intended to identify strong rules discovered in databases using different measures of interestingness. based on the concept of strong rules, rakesh agrawal et al.[2] introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (pos) systems in supermarkets. for example, the rule {onions, potatoes}=>{burger} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. 1 10 relations NNS logistic regression was developed by statistician david cox in 1958[2][3](although much work was done in the single independent variable case almost two decades earlier). the binary logistic model is used to estimate the probability of a binary response based on one or more predictor (or independent) variables (features). as such it is not a classification method. it could be called a qualitative response/discrete choice model in the terminology of economics. 1 10 1958[2][3](although JJ decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the items target value. it is one of the predictive modelling approaches used in statistics, data mining and machine learning. tree models where the target variable can take a finite set of values are called classification trees. in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. 1 10 predictive JJ association rule learning is a method for discovering interesting relations between variables in large databases. it is intended to identify strong rules discovered in databases using different measures of interestingness. based on the concept of strong rules, rakesh agrawal et al.[2] introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (pos) systems in supermarkets. for example, the rule {onions, potatoes}=>{burger} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. 1 11 between IN decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the items target value. it is one of the predictive modelling approaches used in statistics, data mining and machine learning. tree models where the target variable can take a finite set of values are called classification trees. in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. 1 11 model NN logistic regression was developed by statistician david cox in 1958[2][3](although much work was done in the single independent variable case almost two decades earlier). the binary logistic model is used to estimate the probability of a binary response based on one or more predictor (or independent) variables (features). as such it is not a classification method. it could be called a qualitative response/discrete choice model in the terminology of economics. 1 11 much JJ cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 11 set NN in statistics, simple linear regression is the least squares estimator of a linear regression model with a single explanatory variable. in other words, simple linear regression fits a straight line through the set of n points in such a way that makes the sum of squared residuals of the model (that is, vertical distances between the points of the data set and the fitted line) as small as possible. 1 11 estimator NN in statistics, simple linear regression is the least squares estimator of a linear regression model with a single explanatory variable. in other words, simple linear regression fits a straight line through the set of n points in such a way that makes the sum of squared residuals of the model (that is, vertical distances between the points of the data set and the fitted line) as small as possible. 1 12 of IN cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 12 of IN association rule learning is a method for discovering interesting relations between variables in large databases. it is intended to identify strong rules discovered in databases using different measures of interestingness. based on the concept of strong rules, rakesh agrawal et al.[2] introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (pos) systems in supermarkets. for example, the rule {onions, potatoes}=>{burger} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. 1 12 variables NNS decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the items target value. it is one of the predictive modelling approaches used in statistics, data mining and machine learning. tree models where the target variable can take a finite set of values are called classification trees. in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. 1 12 which WDT logistic regression was developed by statistician david cox in 1958[2][3](although much work was done in the single independent variable case almost two decades earlier). the binary logistic model is used to estimate the probability of a binary response based on one or more predictor (or independent) variables (features). as such it is not a classification method. it could be called a qualitative response/discrete choice model in the terminology of economics. 1 12 work NN decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the items target value. it is one of the predictive modelling approaches used in statistics, data mining and machine learning. tree models where the target variable can take a finite set of values are called classification trees. in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. 1 13 maps VBZ cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 13 objects NNS association rule learning is a method for discovering interesting relations between variables in large databases. it is intended to identify strong rules discovered in databases using different measures of interestingness. based on the concept of strong rules, rakesh agrawal et al.[2] introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (pos) systems in supermarkets. for example, the rule {onions, potatoes}=>{burger} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. 1 13 in IN logistic regression was developed by statistician david cox in 1958[2][3](although much work was done in the single independent variable case almost two decades earlier). the binary logistic model is used to estimate the probability of a binary response based on one or more predictor (or independent) variables (features). as such it is not a classification method. it could be called a qualitative response/discrete choice model in the terminology of economics. 1 13 was VBD in statistics, simple linear regression is the least squares estimator of a linear regression model with a single explanatory variable. in other words, simple linear regression fits a straight line through the set of n points in such a way that makes the sum of squared residuals of the model (that is, vertical distances between the points of the data set and the fitted line) as small as possible. 1 13 a DT cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 14 in IN logistic regression was developed by statistician david cox in 1958[2][3](although much work was done in the single independent variable case almost two decades earlier). the binary logistic model is used to estimate the probability of a binary response based on one or more predictor (or independent) variables (features). as such it is not a classification method. it could be called a qualitative response/discrete choice model in the terminology of economics. 1 14 done VBN decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the items target value. it is one of the predictive modelling approaches used in statistics, data mining and machine learning. tree models where the target variable can take a finite set of values are called classification trees. in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. 1 14 observations NNS association rule learning is a method for discovering interesting relations between variables in large databases. it is intended to identify strong rules discovered in databases using different measures of interestingness. based on the concept of strong rules, rakesh agrawal et al.[2] introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (pos) systems in supermarkets. for example, the rule {onions, potatoes}=>{burger} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. 1 14 large JJ in statistics, simple linear regression is the least squares estimator of a linear regression model with a single explanatory variable. in other words, simple linear regression fits a straight line through the set of n points in such a way that makes the sum of squared residuals of the model (that is, vertical distances between the points of the data set and the fitted line) as small as possible. 1 14 linear JJ in statistics, simple linear regression is the least squares estimator of a linear regression model with a single explanatory variable. in other words, simple linear regression fits a straight line through the set of n points in such a way that makes the sum of squared residuals of the model (that is, vertical distances between the points of the data set and the fitted line) as small as possible. 1 15 regression NN cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 15 such PDT decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the items target value. it is one of the predictive modelling approaches used in statistics, data mining and machine learning. tree models where the target variable can take a finite set of values are called classification trees. in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. 1 15 about IN logistic regression was developed by statistician david cox in 1958[2][3](although much work was done in the single independent variable case almost two decades earlier). the binary logistic model is used to estimate the probability of a binary response based on one or more predictor (or independent) variables (features). as such it is not a classification method. it could be called a qualitative response/discrete choice model in the terminology of economics. 1 15 in IN association rule learning is a method for discovering interesting relations between variables in large databases. it is intended to identify strong rules discovered in databases using different measures of interestingness. based on the concept of strong rules, rakesh agrawal et al.[2] introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (pos) systems in supermarkets. for example, the rule {onions, potatoes}=>{burger} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. 1 15 databases NNS logistic regression was developed by statistician david cox in 1958[2][3](although much work was done in the single independent variable case almost two decades earlier). the binary logistic model is used to estimate the probability of a binary response based on one or more predictor (or independent) variables (features). as such it is not a classification method. it could be called a qualitative response/discrete choice model in the terminology of economics. 1 16 the DT association rule learning is a method for discovering interesting relations between variables in large databases. it is intended to identify strong rules discovered in databases using different measures of interestingness. based on the concept of strong rules, rakesh agrawal et al.[2] introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (pos) systems in supermarkets. for example, the rule {onions, potatoes}=>{burger} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. 1 16 . O cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 16 a DT in statistics, simple linear regression is the least squares estimator of a linear regression model with a single explanatory variable. in other words, simple linear regression fits a straight line through the set of n points in such a way that makes the sum of squared residuals of the model (that is, vertical distances between the points of the data set and the fitted line) as small as possible. 1 16 model NN decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the items target value. it is one of the predictive modelling approaches used in statistics, data mining and machine learning. tree models where the target variable can take a finite set of values are called classification trees. in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. 1 16 an DT cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 17 way NN in statistics, simple linear regression is the least squares estimator of a linear regression model with a single explanatory variable. in other words, simple linear regression fits a straight line through the set of n points in such a way that makes the sum of squared residuals of the model (that is, vertical distances between the points of the data set and the fitted line) as small as possible. 1 17 with IN decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the items target value. it is one of the predictive modelling approaches used in statistics, data mining and machine learning. tree models where the target variable can take a finite set of values are called classification trees. in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. 1 17 item NN logistic regression was developed by statistician david cox in 1958[2][3](although much work was done in the single independent variable case almost two decades earlier). the binary logistic model is used to estimate the probability of a binary response based on one or more predictor (or independent) variables (features). as such it is not a classification method. it could be called a qualitative response/discrete choice model in the terminology of economics. 1 17 single JJ association rule learning is a method for discovering interesting relations between variables in large databases. it is intended to identify strong rules discovered in databases using different measures of interestingness. based on the concept of strong rules, rakesh agrawal et al.[2] introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (pos) systems in supermarkets. for example, the rule {onions, potatoes}=>{burger} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. 1 17 it PRP cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 18 that WDT decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the items target value. it is one of the predictive modelling approaches used in statistics, data mining and machine learning. tree models where the target variable can take a finite set of values are called classification trees. in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. 1 18 to TO logistic regression was developed by statistician david cox in 1958[2][3](although much work was done in the single independent variable case almost two decades earlier). the binary logistic model is used to estimate the probability of a binary response based on one or more predictor (or independent) variables (features). as such it is not a classification method. it could be called a qualitative response/discrete choice model in the terminology of economics. 1 18 independent JJ association rule learning is a method for discovering interesting relations between variables in large databases. it is intended to identify strong rules discovered in databases using different measures of interestingness. based on the concept of strong rules, rakesh agrawal et al.[2] introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (pos) systems in supermarkets. for example, the rule {onions, potatoes}=>{burger} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. 1 18 is VBZ in statistics, simple linear regression is the least squares estimator of a linear regression model with a single explanatory variable. in other words, simple linear regression fits a straight line through the set of n points in such a way that makes the sum of squared residuals of the model (that is, vertical distances between the points of the data set and the fitted line) as small as possible. 1 18 a DT logistic regression was developed by statistician david cox in 1958[2][3](although much work was done in the single independent variable case almost two decades earlier). the binary logistic model is used to estimate the probability of a binary response based on one or more predictor (or independent) variables (features). as such it is not a classification method. it could be called a qualitative response/discrete choice model in the terminology of economics. 1 19 variable JJ in statistics, simple linear regression is the least squares estimator of a linear regression model with a single explanatory variable. in other words, simple linear regression fits a straight line through the set of n points in such a way that makes the sum of squared residuals of the model (that is, vertical distances between the points of the data set and the fitted line) as small as possible. 1 19 single JJ cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 19 objects VBZ decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the items target value. it is one of the predictive modelling approaches used in statistics, data mining and machine learning. tree models where the target variable can take a finite set of values are called classification trees. in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. 1 19 conclusions NNS association rule learning is a method for discovering interesting relations between variables in large databases. it is intended to identify strong rules discovered in databases using different measures of interestingness. based on the concept of strong rules, rakesh agrawal et al.[2] introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (pos) systems in supermarkets. for example, the rule {onions, potatoes}=>{burger} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. 1 19 intended VBN logistic regression was developed by statistician david cox in 1958[2][3](although much work was done in the single independent variable case almost two decades earlier). the binary logistic model is used to estimate the probability of a binary response based on one or more predictor (or independent) variables (features). as such it is not a classification method. it could be called a qualitative response/discrete choice model in the terminology of economics. 1 20 case NN in statistics, simple linear regression is the least squares estimator of a linear regression model with a single explanatory variable. in other words, simple linear regression fits a straight line through the set of n points in such a way that makes the sum of squared residuals of the model (that is, vertical distances between the points of the data set and the fitted line) as small as possible. 1 20 explanatory NN association rule learning is a method for discovering interesting relations between variables in large databases. it is intended to identify strong rules discovered in databases using different measures of interestingness. based on the concept of strong rules, rakesh agrawal et al.[2] introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (pos) systems in supermarkets. for example, the rule {onions, potatoes}=>{burger} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. 1 20 to TO cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 20 in IN decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the items target value. it is one of the predictive modelling approaches used in statistics, data mining and machine learning. tree models where the target variable can take a finite set of values are called classification trees. in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. 1 20 about IN logistic regression was developed by statistician david cox in 1958[2][3](although much work was done in the single independent variable case almost two decades earlier). the binary logistic model is used to estimate the probability of a binary response based on one or more predictor (or independent) variables (features). as such it is not a classification method. it could be called a qualitative response/discrete choice model in the terminology of economics. 1 21 almost RB cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 21 the DT association rule learning is a method for discovering interesting relations between variables in large databases. it is intended to identify strong rules discovered in databases using different measures of interestingness. based on the concept of strong rules, rakesh agrawal et al.[2] introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (pos) systems in supermarkets. for example, the rule {onions, potatoes}=>{burger} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. 1 21 identify VB in statistics, simple linear regression is the least squares estimator of a linear regression model with a single explanatory variable. in other words, simple linear regression fits a straight line through the set of n points in such a way that makes the sum of squared residuals of the model (that is, vertical distances between the points of the data set and the fitted line) as small as possible. 1 21 variable JJ decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the items target value. it is one of the predictive modelling approaches used in statistics, data mining and machine learning. tree models where the target variable can take a finite set of values are called classification trees. in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. 1 21 the DT in statistics, simple linear regression is the least squares estimator of a linear regression model with a single explanatory variable. in other words, simple linear regression fits a straight line through the set of n points in such a way that makes the sum of squared residuals of the model (that is, vertical distances between the points of the data set and the fitted line) as small as possible. 1 22 . O association rule learning is a method for discovering interesting relations between variables in large databases. it is intended to identify strong rules discovered in databases using different measures of interestingness. based on the concept of strong rules, rakesh agrawal et al.[2] introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (pos) systems in supermarkets. for example, the rule {onions, potatoes}=>{burger} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. 1 22 strong JJ decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the items target value. it is one of the predictive modelling approaches used in statistics, data mining and machine learning. tree models where the target variable can take a finite set of values are called classification trees. in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. 1 22 items NNS cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 22 same JJ logistic regression was developed by statistician david cox in 1958[2][3](although much work was done in the single independent variable case almost two decades earlier). the binary logistic model is used to estimate the probability of a binary response based on one or more predictor (or independent) variables (features). as such it is not a classification method. it could be called a qualitative response/discrete choice model in the terminology of economics. 1 22 two CD association rule learning is a method for discovering interesting relations between variables in large databases. it is intended to identify strong rules discovered in databases using different measures of interestingness. based on the concept of strong rules, rakesh agrawal et al.[2] introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (pos) systems in supermarkets. for example, the rule {onions, potatoes}=>{burger} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. 1 23 rules NNS in statistics, simple linear regression is the least squares estimator of a linear regression model with a single explanatory variable. in other words, simple linear regression fits a straight line through the set of n points in such a way that makes the sum of squared residuals of the model (that is, vertical distances between the points of the data set and the fitted line) as small as possible. 1 23 in IN cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 23 group NN decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the items target value. it is one of the predictive modelling approaches used in statistics, data mining and machine learning. tree models where the target variable can take a finite set of values are called classification trees. in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. 1 23 target NN logistic regression was developed by statistician david cox in 1958[2][3](although much work was done in the single independent variable case almost two decades earlier). the binary logistic model is used to estimate the probability of a binary response based on one or more predictor (or independent) variables (features). as such it is not a classification method. it could be called a qualitative response/discrete choice model in the terminology of economics. 1 23 decades NNS cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 24 ( O in statistics, simple linear regression is the least squares estimator of a linear regression model with a single explanatory variable. in other words, simple linear regression fits a straight line through the set of n points in such a way that makes the sum of squared residuals of the model (that is, vertical distances between the points of the data set and the fitted line) as small as possible. 1 24 other JJ decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the items target value. it is one of the predictive modelling approaches used in statistics, data mining and machine learning. tree models where the target variable can take a finite set of values are called classification trees. in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. 1 24 value NN association rule learning is a method for discovering interesting relations between variables in large databases. it is intended to identify strong rules discovered in databases using different measures of interestingness. based on the concept of strong rules, rakesh agrawal et al.[2] introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (pos) systems in supermarkets. for example, the rule {onions, potatoes}=>{burger} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. 1 24 discovered VBN logistic regression was developed by statistician david cox in 1958[2][3](although much work was done in the single independent variable case almost two decades earlier). the binary logistic model is used to estimate the probability of a binary response based on one or more predictor (or independent) variables (features). as such it is not a classification method. it could be called a qualitative response/discrete choice model in the terminology of economics. 1 24 earlier) VBP in statistics, simple linear regression is the least squares estimator of a linear regression model with a single explanatory variable. in other words, simple linear regression fits a straight line through the set of n points in such a way that makes the sum of squared residuals of the model (that is, vertical distances between the points of the data set and the fitted line) as small as possible. 1 25 words NNS logistic regression was developed by statistician david cox in 1958[2][3](although much work was done in the single independent variable case almost two decades earlier). the binary logistic model is used to estimate the probability of a binary response based on one or more predictor (or independent) variables (features). as such it is not a classification method. it could be called a qualitative response/discrete choice model in the terminology of economics. 1 25 . O cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 25 called VBD decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the items target value. it is one of the predictive modelling approaches used in statistics, data mining and machine learning. tree models where the target variable can take a finite set of values are called classification trees. in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. 1 25 . O association rule learning is a method for discovering interesting relations between variables in large databases. it is intended to identify strong rules discovered in databases using different measures of interestingness. based on the concept of strong rules, rakesh agrawal et al.[2] introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (pos) systems in supermarkets. for example, the rule {onions, potatoes}=>{burger} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. 1 25 in IN in statistics, simple linear regression is the least squares estimator of a linear regression model with a single explanatory variable. in other words, simple linear regression fits a straight line through the set of n points in such a way that makes the sum of squared residuals of the model (that is, vertical distances between the points of the data set and the fitted line) as small as possible. 1 26 , O cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 26 a DT association rule learning is a method for discovering interesting relations between variables in large databases. it is intended to identify strong rules discovered in databases using different measures of interestingness. based on the concept of strong rules, rakesh agrawal et al.[2] introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (pos) systems in supermarkets. for example, the rule {onions, potatoes}=>{burger} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. 1 26 databases NNS decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the items target value. it is one of the predictive modelling approaches used in statistics, data mining and machine learning. tree models where the target variable can take a finite set of values are called classification trees. in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. 1 26 it PRP logistic regression was developed by statistician david cox in 1958[2][3](although much work was done in the single independent variable case almost two decades earlier). the binary logistic model is used to estimate the probability of a binary response based on one or more predictor (or independent) variables (features). as such it is not a classification method. it could be called a qualitative response/discrete choice model in the terminology of economics. 1 26 the DT cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 27 cluster NN decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the items target value. it is one of the predictive modelling approaches used in statistics, data mining and machine learning. tree models where the target variable can take a finite set of values are called classification trees. in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. 1 27 is VBZ association rule learning is a method for discovering interesting relations between variables in large databases. it is intended to identify strong rules discovered in databases using different measures of interestingness. based on the concept of strong rules, rakesh agrawal et al.[2] introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (pos) systems in supermarkets. for example, the rule {onions, potatoes}=>{burger} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. 1 27 using VBG in statistics, simple linear regression is the least squares estimator of a linear regression model with a single explanatory variable. in other words, simple linear regression fits a straight line through the set of n points in such a way that makes the sum of squared residuals of the model (that is, vertical distances between the points of the data set and the fitted line) as small as possible. 1 27 simple JJ logistic regression was developed by statistician david cox in 1958[2][3](although much work was done in the single independent variable case almost two decades earlier). the binary logistic model is used to estimate the probability of a binary response based on one or more predictor (or independent) variables (features). as such it is not a classification method. it could be called a qualitative response/discrete choice model in the terminology of economics. 1 27 binary JJ in statistics, simple linear regression is the least squares estimator of a linear regression model with a single explanatory variable. in other words, simple linear regression fits a straight line through the set of n points in such a way that makes the sum of squared residuals of the model (that is, vertical distances between the points of the data set and the fitted line) as small as possible. 1 28 linear JJ cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 28 ) O association rule learning is a method for discovering interesting relations between variables in large databases. it is intended to identify strong rules discovered in databases using different measures of interestingness. based on the concept of strong rules, rakesh agrawal et al.[2] introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (pos) systems in supermarkets. for example, the rule {onions, potatoes}=>{burger} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. 1 28 different JJ logistic regression was developed by statistician david cox in 1958[2][3](although much work was done in the single independent variable case almost two decades earlier). the binary logistic model is used to estimate the probability of a binary response based on one or more predictor (or independent) variables (features). as such it is not a classification method. it could be called a qualitative response/discrete choice model in the terminology of economics. 1 28 logistic JJ decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the items target value. it is one of the predictive modelling approaches used in statistics, data mining and machine learning. tree models where the target variable can take a finite set of values are called classification trees. in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. 1 28 one CD in statistics, simple linear regression is the least squares estimator of a linear regression model with a single explanatory variable. in other words, simple linear regression fits a straight line through the set of n points in such a way that makes the sum of squared residuals of the model (that is, vertical distances between the points of the data set and the fitted line) as small as possible. 1 29 regression NN cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 29 are VBP decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the items target value. it is one of the predictive modelling approaches used in statistics, data mining and machine learning. tree models where the target variable can take a finite set of values are called classification trees. in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. 1 29 of IN logistic regression was developed by statistician david cox in 1958[2][3](although much work was done in the single independent variable case almost two decades earlier). the binary logistic model is used to estimate the probability of a binary response based on one or more predictor (or independent) variables (features). as such it is not a classification method. it could be called a qualitative response/discrete choice model in the terminology of economics. 1 29 model NN association rule learning is a method for discovering interesting relations between variables in large databases. it is intended to identify strong rules discovered in databases using different measures of interestingness. based on the concept of strong rules, rakesh agrawal et al.[2] introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (pos) systems in supermarkets. for example, the rule {onions, potatoes}=>{burger} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. 1 29 measures NNS cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 30 more RBR decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the items target value. it is one of the predictive modelling approaches used in statistics, data mining and machine learning. tree models where the target variable can take a finite set of values are called classification trees. in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. 1 30 the DT in statistics, simple linear regression is the least squares estimator of a linear regression model with a single explanatory variable. in other words, simple linear regression fits a straight line through the set of n points in such a way that makes the sum of squared residuals of the model (that is, vertical distances between the points of the data set and the fitted line) as small as possible. 1 30 fits VBZ association rule learning is a method for discovering interesting relations between variables in large databases. it is intended to identify strong rules discovered in databases using different measures of interestingness. based on the concept of strong rules, rakesh agrawal et al.[2] introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (pos) systems in supermarkets. for example, the rule {onions, potatoes}=>{burger} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. 1 30 of IN logistic regression was developed by statistician david cox in 1958[2][3](although much work was done in the single independent variable case almost two decades earlier). the binary logistic model is used to estimate the probability of a binary response based on one or more predictor (or independent) variables (features). as such it is not a classification method. it could be called a qualitative response/discrete choice model in the terminology of economics. 1 30 is VBZ cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 31 similar JJ decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the items target value. it is one of the predictive modelling approaches used in statistics, data mining and machine learning. tree models where the target variable can take a finite set of values are called classification trees. in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. 1 31 predictive JJ association rule learning is a method for discovering interesting relations between variables in large databases. it is intended to identify strong rules discovered in databases using different measures of interestingness. based on the concept of strong rules, rakesh agrawal et al.[2] introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (pos) systems in supermarkets. for example, the rule {onions, potatoes}=>{burger} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. 1 31 interestingness NN in statistics, simple linear regression is the least squares estimator of a linear regression model with a single explanatory variable. in other words, simple linear regression fits a straight line through the set of n points in such a way that makes the sum of squared residuals of the model (that is, vertical distances between the points of the data set and the fitted line) as small as possible. 1 31 a DT logistic regression was developed by statistician david cox in 1958[2][3](although much work was done in the single independent variable case almost two decades earlier). the binary logistic model is used to estimate the probability of a binary response based on one or more predictor (or independent) variables (features). as such it is not a classification method. it could be called a qualitative response/discrete choice model in the terminology of economics. 1 31 used VBN in statistics, simple linear regression is the least squares estimator of a linear regression model with a single explanatory variable. in other words, simple linear regression fits a straight line through the set of n points in such a way that makes the sum of squared residuals of the model (that is, vertical distances between the points of the data set and the fitted line) as small as possible. 1 32 straight JJ decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the items target value. it is one of the predictive modelling approaches used in statistics, data mining and machine learning. tree models where the target variable can take a finite set of values are called classification trees. in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. 1 32 modelling JJ association rule learning is a method for discovering interesting relations between variables in large databases. it is intended to identify strong rules discovered in databases using different measures of interestingness. based on the concept of strong rules, rakesh agrawal et al.[2] introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (pos) systems in supermarkets. for example, the rule {onions, potatoes}=>{burger} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. 1 32 . O logistic regression was developed by statistician david cox in 1958[2][3](although much work was done in the single independent variable case almost two decades earlier). the binary logistic model is used to estimate the probability of a binary response based on one or more predictor (or independent) variables (features). as such it is not a classification method. it could be called a qualitative response/discrete choice model in the terminology of economics. 1 32 to TO cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 32 ( O decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the items target value. it is one of the predictive modelling approaches used in statistics, data mining and machine learning. tree models where the target variable can take a finite set of values are called classification trees. in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. 1 33 approaches NNS in statistics, simple linear regression is the least squares estimator of a linear regression model with a single explanatory variable. in other words, simple linear regression fits a straight line through the set of n points in such a way that makes the sum of squared residuals of the model (that is, vertical distances between the points of the data set and the fitted line) as small as possible. 1 33 line NN association rule learning is a method for discovering interesting relations between variables in large databases. it is intended to identify strong rules discovered in databases using different measures of interestingness. based on the concept of strong rules, rakesh agrawal et al.[2] introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (pos) systems in supermarkets. for example, the rule {onions, potatoes}=>{burger} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. 1 33 based VBN cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 33 in IN logistic regression was developed by statistician david cox in 1958[2][3](although much work was done in the single independent variable case almost two decades earlier). the binary logistic model is used to estimate the probability of a binary response based on one or more predictor (or independent) variables (features). as such it is not a classification method. it could be called a qualitative response/discrete choice model in the terminology of economics. 1 33 estimate VB in statistics, simple linear regression is the least squares estimator of a linear regression model with a single explanatory variable. in other words, simple linear regression fits a straight line through the set of n points in such a way that makes the sum of squared residuals of the model (that is, vertical distances between the points of the data set and the fitted line) as small as possible. 1 34 through IN decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the items target value. it is one of the predictive modelling approaches used in statistics, data mining and machine learning. tree models where the target variable can take a finite set of values are called classification trees. in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. 1 34 used VBN cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 34 some DT association rule learning is a method for discovering interesting relations between variables in large databases. it is intended to identify strong rules discovered in databases using different measures of interestingness. based on the concept of strong rules, rakesh agrawal et al.[2] introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (pos) systems in supermarkets. for example, the rule {onions, potatoes}=>{burger} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. 1 34 on IN logistic regression was developed by statistician david cox in 1958[2][3](although much work was done in the single independent variable case almost two decades earlier). the binary logistic model is used to estimate the probability of a binary response based on one or more predictor (or independent) variables (features). as such it is not a classification method. it could be called a qualitative response/discrete choice model in the terminology of economics. 1 34 the DT in statistics, simple linear regression is the least squares estimator of a linear regression model with a single explanatory variable. in other words, simple linear regression fits a straight line through the set of n points in such a way that makes the sum of squared residuals of the model (that is, vertical distances between the points of the data set and the fitted line) as small as possible. 1 35 the DT decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the items target value. it is one of the predictive modelling approaches used in statistics, data mining and machine learning. tree models where the target variable can take a finite set of values are called classification trees. in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. 1 35 in IN association rule learning is a method for discovering interesting relations between variables in large databases. it is intended to identify strong rules discovered in databases using different measures of interestingness. based on the concept of strong rules, rakesh agrawal et al.[2] introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (pos) systems in supermarkets. for example, the rule {onions, potatoes}=>{burger} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. 1 35 the DT cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 35 sense NN logistic regression was developed by statistician david cox in 1958[2][3](although much work was done in the single independent variable case almost two decades earlier). the binary logistic model is used to estimate the probability of a binary response based on one or more predictor (or independent) variables (features). as such it is not a classification method. it could be called a qualitative response/discrete choice model in the terminology of economics. 1 35 probability NN in statistics, simple linear regression is the least squares estimator of a linear regression model with a single explanatory variable. in other words, simple linear regression fits a straight line through the set of n points in such a way that makes the sum of squared residuals of the model (that is, vertical distances between the points of the data set and the fitted line) as small as possible. 1 36 set NN association rule learning is a method for discovering interesting relations between variables in large databases. it is intended to identify strong rules discovered in databases using different measures of interestingness. based on the concept of strong rules, rakesh agrawal et al.[2] introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (pos) systems in supermarkets. for example, the rule {onions, potatoes}=>{burger} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. 1 36 concept NN decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the items target value. it is one of the predictive modelling approaches used in statistics, data mining and machine learning. tree models where the target variable can take a finite set of values are called classification trees. in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. 1 36 statistics NNS cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 36 or CC logistic regression was developed by statistician david cox in 1958[2][3](although much work was done in the single independent variable case almost two decades earlier). the binary logistic model is used to estimate the probability of a binary response based on one or more predictor (or independent) variables (features). as such it is not a classification method. it could be called a qualitative response/discrete choice model in the terminology of economics. 1 36 of IN logistic regression was developed by statistician david cox in 1958[2][3](although much work was done in the single independent variable case almost two decades earlier). the binary logistic model is used to estimate the probability of a binary response based on one or more predictor (or independent) variables (features). as such it is not a classification method. it could be called a qualitative response/discrete choice model in the terminology of economics. 1 37 a DT in statistics, simple linear regression is the least squares estimator of a linear regression model with a single explanatory variable. in other words, simple linear regression fits a straight line through the set of n points in such a way that makes the sum of squared residuals of the model (that is, vertical distances between the points of the data set and the fitted line) as small as possible. 1 37 of IN decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the items target value. it is one of the predictive modelling approaches used in statistics, data mining and machine learning. tree models where the target variable can take a finite set of values are called classification trees. in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. 1 37 , O association rule learning is a method for discovering interesting relations between variables in large databases. it is intended to identify strong rules discovered in databases using different measures of interestingness. based on the concept of strong rules, rakesh agrawal et al.[2] introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (pos) systems in supermarkets. for example, the rule {onions, potatoes}=>{burger} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. 1 37 of IN cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 37 another DT in statistics, simple linear regression is the least squares estimator of a linear regression model with a single explanatory variable. in other words, simple linear regression fits a straight line through the set of n points in such a way that makes the sum of squared residuals of the model (that is, vertical distances between the points of the data set and the fitted line) as small as possible. 1 38 n JJ association rule learning is a method for discovering interesting relations between variables in large databases. it is intended to identify strong rules discovered in databases using different measures of interestingness. based on the concept of strong rules, rakesh agrawal et al.[2] introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (pos) systems in supermarkets. for example, the rule {onions, potatoes}=>{burger} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. 1 38 strong JJ cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 38 ) O decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the items target value. it is one of the predictive modelling approaches used in statistics, data mining and machine learning. tree models where the target variable can take a finite set of values are called classification trees. in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. 1 38 data NN logistic regression was developed by statistician david cox in 1958[2][3](although much work was done in the single independent variable case almost two decades earlier). the binary logistic model is used to estimate the probability of a binary response based on one or more predictor (or independent) variables (features). as such it is not a classification method. it could be called a qualitative response/discrete choice model in the terminology of economics. 1 38 binary JJ in statistics, simple linear regression is the least squares estimator of a linear regression model with a single explanatory variable. in other words, simple linear regression fits a straight line through the set of n points in such a way that makes the sum of squared residuals of the model (that is, vertical distances between the points of the data set and the fitted line) as small as possible. 1 39 points NNS decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the items target value. it is one of the predictive modelling approaches used in statistics, data mining and machine learning. tree models where the target variable can take a finite set of values are called classification trees. in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. 1 39 mining NN logistic regression was developed by statistician david cox in 1958[2][3](although much work was done in the single independent variable case almost two decades earlier). the binary logistic model is used to estimate the probability of a binary response based on one or more predictor (or independent) variables (features). as such it is not a classification method. it could be called a qualitative response/discrete choice model in the terminology of economics. 1 39 response NN association rule learning is a method for discovering interesting relations between variables in large databases. it is intended to identify strong rules discovered in databases using different measures of interestingness. based on the concept of strong rules, rakesh agrawal et al.[2] introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (pos) systems in supermarkets. for example, the rule {onions, potatoes}=>{burger} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. 1 39 rules NNS cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 39 to TO decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the items target value. it is one of the predictive modelling approaches used in statistics, data mining and machine learning. tree models where the target variable can take a finite set of values are called classification trees. in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. 1 40 and CC association rule learning is a method for discovering interesting relations between variables in large databases. it is intended to identify strong rules discovered in databases using different measures of interestingness. based on the concept of strong rules, rakesh agrawal et al.[2] introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (pos) systems in supermarkets. for example, the rule {onions, potatoes}=>{burger} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. 1 40 , O cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 40 each DT in statistics, simple linear regression is the least squares estimator of a linear regression model with a single explanatory variable. in other words, simple linear regression fits a straight line through the set of n points in such a way that makes the sum of squared residuals of the model (that is, vertical distances between the points of the data set and the fitted line) as small as possible. 1 40 in IN logistic regression was developed by statistician david cox in 1958[2][3](although much work was done in the single independent variable case almost two decades earlier). the binary logistic model is used to estimate the probability of a binary response based on one or more predictor (or independent) variables (features). as such it is not a classification method. it could be called a qualitative response/discrete choice model in the terminology of economics. 1 40 based VBN in statistics, simple linear regression is the least squares estimator of a linear regression model with a single explanatory variable. in other words, simple linear regression fits a straight line through the set of n points in such a way that makes the sum of squared residuals of the model (that is, vertical distances between the points of the data set and the fitted line) as small as possible. 1 41 such PDT decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the items target value. it is one of the predictive modelling approaches used in statistics, data mining and machine learning. tree models where the target variable can take a finite set of values are called classification trees. in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. 1 41 machine NN association rule learning is a method for discovering interesting relations between variables in large databases. it is intended to identify strong rules discovered in databases using different measures of interestingness. based on the concept of strong rules, rakesh agrawal et al.[2] introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (pos) systems in supermarkets. for example, the rule {onions, potatoes}=>{burger} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. 1 41 rakesh JJ cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 41 other JJ logistic regression was developed by statistician david cox in 1958[2][3](although much work was done in the single independent variable case almost two decades earlier). the binary logistic model is used to estimate the probability of a binary response based on one or more predictor (or independent) variables (features). as such it is not a classification method. it could be called a qualitative response/discrete choice model in the terminology of economics. 1 41 on IN logistic regression was developed by statistician david cox in 1958[2][3](although much work was done in the single independent variable case almost two decades earlier). the binary logistic model is used to estimate the probability of a binary response based on one or more predictor (or independent) variables (features). as such it is not a classification method. it could be called a qualitative response/discrete choice model in the terminology of economics. 1 42 one CD in statistics, simple linear regression is the least squares estimator of a linear regression model with a single explanatory variable. in other words, simple linear regression fits a straight line through the set of n points in such a way that makes the sum of squared residuals of the model (that is, vertical distances between the points of the data set and the fitted line) as small as possible. 1 42 a DT decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the items target value. it is one of the predictive modelling approaches used in statistics, data mining and machine learning. tree models where the target variable can take a finite set of values are called classification trees. in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. 1 42 learning VBG association rule learning is a method for discovering interesting relations between variables in large databases. it is intended to identify strong rules discovered in databases using different measures of interestingness. based on the concept of strong rules, rakesh agrawal et al.[2] introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (pos) systems in supermarkets. for example, the rule {onions, potatoes}=>{burger} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. 1 42 agrawal JJ cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 42 than IN in statistics, simple linear regression is the least squares estimator of a linear regression model with a single explanatory variable. in other words, simple linear regression fits a straight line through the set of n points in such a way that makes the sum of squared residuals of the model (that is, vertical distances between the points of the data set and the fitted line) as small as possible. 1 43 way NN decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the items target value. it is one of the predictive modelling approaches used in statistics, data mining and machine learning. tree models where the target variable can take a finite set of values are called classification trees. in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. 1 43 . O cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 43 to TO association rule learning is a method for discovering interesting relations between variables in large databases. it is intended to identify strong rules discovered in databases using different measures of interestingness. based on the concept of strong rules, rakesh agrawal et al.[2] introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (pos) systems in supermarkets. for example, the rule {onions, potatoes}=>{burger} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. 1 43 et NN logistic regression was developed by statistician david cox in 1958[2][3](although much work was done in the single independent variable case almost two decades earlier). the binary logistic model is used to estimate the probability of a binary response based on one or more predictor (or independent) variables (features). as such it is not a classification method. it could be called a qualitative response/discrete choice model in the terminology of economics. 1 43 or CC cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 44 those DT in statistics, simple linear regression is the least squares estimator of a linear regression model with a single explanatory variable. in other words, simple linear regression fits a straight line through the set of n points in such a way that makes the sum of squared residuals of the model (that is, vertical distances between the points of the data set and the fitted line) as small as possible. 1 44 that WDT decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the items target value. it is one of the predictive modelling approaches used in statistics, data mining and machine learning. tree models where the target variable can take a finite set of values are called classification trees. in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. 1 44 tree CD association rule learning is a method for discovering interesting relations between variables in large databases. it is intended to identify strong rules discovered in databases using different measures of interestingness. based on the concept of strong rules, rakesh agrawal et al.[2] introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (pos) systems in supermarkets. for example, the rule {onions, potatoes}=>{burger} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. 1 44 al.[2 NN logistic regression was developed by statistician david cox in 1958[2][3](although much work was done in the single independent variable case almost two decades earlier). the binary logistic model is used to estimate the probability of a binary response based on one or more predictor (or independent) variables (features). as such it is not a classification method. it could be called a qualitative response/discrete choice model in the terminology of economics. 1 44 more JJR decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the items target value. it is one of the predictive modelling approaches used in statistics, data mining and machine learning. tree models where the target variable can take a finite set of values are called classification trees. in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. 1 45 models NNS in statistics, simple linear regression is the least squares estimator of a linear regression model with a single explanatory variable. in other words, simple linear regression fits a straight line through the set of n points in such a way that makes the sum of squared residuals of the model (that is, vertical distances between the points of the data set and the fitted line) as small as possible. 1 45 makes VBZ association rule learning is a method for discovering interesting relations between variables in large databases. it is intended to identify strong rules discovered in databases using different measures of interestingness. based on the concept of strong rules, rakesh agrawal et al.[2] introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (pos) systems in supermarkets. for example, the rule {onions, potatoes}=>{burger} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. 1 45 ] O cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 45 in IN logistic regression was developed by statistician david cox in 1958[2][3](although much work was done in the single independent variable case almost two decades earlier). the binary logistic model is used to estimate the probability of a binary response based on one or more predictor (or independent) variables (features). as such it is not a classification method. it could be called a qualitative response/discrete choice model in the terminology of economics. 1 45 predictor NN logistic regression was developed by statistician david cox in 1958[2][3](although much work was done in the single independent variable case almost two decades earlier). the binary logistic model is used to estimate the probability of a binary response based on one or more predictor (or independent) variables (features). as such it is not a classification method. it could be called a qualitative response/discrete choice model in the terminology of economics. 1 46 ( O in statistics, simple linear regression is the least squares estimator of a linear regression model with a single explanatory variable. in other words, simple linear regression fits a straight line through the set of n points in such a way that makes the sum of squared residuals of the model (that is, vertical distances between the points of the data set and the fitted line) as small as possible. 1 46 the DT cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 46 other JJ association rule learning is a method for discovering interesting relations between variables in large databases. it is intended to identify strong rules discovered in databases using different measures of interestingness. based on the concept of strong rules, rakesh agrawal et al.[2] introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (pos) systems in supermarkets. for example, the rule {onions, potatoes}=>{burger} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. 1 46 introduced JJ decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the items target value. it is one of the predictive modelling approaches used in statistics, data mining and machine learning. tree models where the target variable can take a finite set of values are called classification trees. in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. 1 46 where WRB in statistics, simple linear regression is the least squares estimator of a linear regression model with a single explanatory variable. in other words, simple linear regression fits a straight line through the set of n points in such a way that makes the sum of squared residuals of the model (that is, vertical distances between the points of the data set and the fitted line) as small as possible. 1 47 sum NN association rule learning is a method for discovering interesting relations between variables in large databases. it is intended to identify strong rules discovered in databases using different measures of interestingness. based on the concept of strong rules, rakesh agrawal et al.[2] introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (pos) systems in supermarkets. for example, the rule {onions, potatoes}=>{burger} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. 1 47 association NN cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 47 groups NNS decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the items target value. it is one of the predictive modelling approaches used in statistics, data mining and machine learning. tree models where the target variable can take a finite set of values are called classification trees. in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. 1 47 the DT logistic regression was developed by statistician david cox in 1958[2][3](although much work was done in the single independent variable case almost two decades earlier). the binary logistic model is used to estimate the probability of a binary response based on one or more predictor (or independent) variables (features). as such it is not a classification method. it could be called a qualitative response/discrete choice model in the terminology of economics. 1 47 or CC cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 48 ( O association rule learning is a method for discovering interesting relations between variables in large databases. it is intended to identify strong rules discovered in databases using different measures of interestingness. based on the concept of strong rules, rakesh agrawal et al.[2] introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (pos) systems in supermarkets. for example, the rule {onions, potatoes}=>{burger} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. 1 48 rules NNS in statistics, simple linear regression is the least squares estimator of a linear regression model with a single explanatory variable. in other words, simple linear regression fits a straight line through the set of n points in such a way that makes the sum of squared residuals of the model (that is, vertical distances between the points of the data set and the fitted line) as small as possible. 1 48 of IN logistic regression was developed by statistician david cox in 1958[2][3](although much work was done in the single independent variable case almost two decades earlier). the binary logistic model is used to estimate the probability of a binary response based on one or more predictor (or independent) variables (features). as such it is not a classification method. it could be called a qualitative response/discrete choice model in the terminology of economics. 1 48 independent JJ decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the items target value. it is one of the predictive modelling approaches used in statistics, data mining and machine learning. tree models where the target variable can take a finite set of values are called classification trees. in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. 1 48 target NN in statistics, simple linear regression is the least squares estimator of a linear regression model with a single explanatory variable. in other words, simple linear regression fits a straight line through the set of n points in such a way that makes the sum of squared residuals of the model (that is, vertical distances between the points of the data set and the fitted line) as small as possible. 1 49 squared JJ cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 49 clusters) NN decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the items target value. it is one of the predictive modelling approaches used in statistics, data mining and machine learning. tree models where the target variable can take a finite set of values are called classification trees. in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. 1 49 variable NN association rule learning is a method for discovering interesting relations between variables in large databases. it is intended to identify strong rules discovered in databases using different measures of interestingness. based on the concept of strong rules, rakesh agrawal et al.[2] introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (pos) systems in supermarkets. for example, the rule {onions, potatoes}=>{burger} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. 1 49 for IN logistic regression was developed by statistician david cox in 1958[2][3](although much work was done in the single independent variable case almost two decades earlier). the binary logistic model is used to estimate the probability of a binary response based on one or more predictor (or independent) variables (features). as such it is not a classification method. it could be called a qualitative response/discrete choice model in the terminology of economics. 1 49 ) O association rule learning is a method for discovering interesting relations between variables in large databases. it is intended to identify strong rules discovered in databases using different measures of interestingness. based on the concept of strong rules, rakesh agrawal et al.[2] introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (pos) systems in supermarkets. for example, the rule {onions, potatoes}=>{burger} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. 1 50 discovering VBG in statistics, simple linear regression is the least squares estimator of a linear regression model with a single explanatory variable. in other words, simple linear regression fits a straight line through the set of n points in such a way that makes the sum of squared residuals of the model (that is, vertical distances between the points of the data set and the fitted line) as small as possible. 1 50 residuals NNS cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 50 . O decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the items target value. it is one of the predictive modelling approaches used in statistics, data mining and machine learning. tree models where the target variable can take a finite set of values are called classification trees. in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. 1 50 can MD logistic regression was developed by statistician david cox in 1958[2][3](although much work was done in the single independent variable case almost two decades earlier). the binary logistic model is used to estimate the probability of a binary response based on one or more predictor (or independent) variables (features). as such it is not a classification method. it could be called a qualitative response/discrete choice model in the terminology of economics. 1 50 variables VBZ association rule learning is a method for discovering interesting relations between variables in large databases. it is intended to identify strong rules discovered in databases using different measures of interestingness. based on the concept of strong rules, rakesh agrawal et al.[2] introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (pos) systems in supermarkets. for example, the rule {onions, potatoes}=>{burger} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. 1 51 regularities NNS in statistics, simple linear regression is the least squares estimator of a linear regression model with a single explanatory variable. in other words, simple linear regression fits a straight line through the set of n points in such a way that makes the sum of squared residuals of the model (that is, vertical distances between the points of the data set and the fitted line) as small as possible. 1 51 of IN cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 51 it PRP decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the items target value. it is one of the predictive modelling approaches used in statistics, data mining and machine learning. tree models where the target variable can take a finite set of values are called classification trees. in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. 1 51 take VB logistic regression was developed by statistician david cox in 1958[2][3](although much work was done in the single independent variable case almost two decades earlier). the binary logistic model is used to estimate the probability of a binary response based on one or more predictor (or independent) variables (features). as such it is not a classification method. it could be called a qualitative response/discrete choice model in the terminology of economics. 1 51 ( O association rule learning is a method for discovering interesting relations between variables in large databases. it is intended to identify strong rules discovered in databases using different measures of interestingness. based on the concept of strong rules, rakesh agrawal et al.[2] introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (pos) systems in supermarkets. for example, the rule {onions, potatoes}=>{burger} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. 1 52 between IN in statistics, simple linear regression is the least squares estimator of a linear regression model with a single explanatory variable. in other words, simple linear regression fits a straight line through the set of n points in such a way that makes the sum of squared residuals of the model (that is, vertical distances between the points of the data set and the fitted line) as small as possible. 1 52 the DT cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 52 is VBZ decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the items target value. it is one of the predictive modelling approaches used in statistics, data mining and machine learning. tree models where the target variable can take a finite set of values are called classification trees. in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. 1 52 a DT logistic regression was developed by statistician david cox in 1958[2][3](although much work was done in the single independent variable case almost two decades earlier). the binary logistic model is used to estimate the probability of a binary response based on one or more predictor (or independent) variables (features). as such it is not a classification method. it could be called a qualitative response/discrete choice model in the terminology of economics. 1 52 features) NN in statistics, simple linear regression is the least squares estimator of a linear regression model with a single explanatory variable. in other words, simple linear regression fits a straight line through the set of n points in such a way that makes the sum of squared residuals of the model (that is, vertical distances between the points of the data set and the fitted line) as small as possible. 1 53 model NN cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 53 a DT association rule learning is a method for discovering interesting relations between variables in large databases. it is intended to identify strong rules discovered in databases using different measures of interestingness. based on the concept of strong rules, rakesh agrawal et al.[2] introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (pos) systems in supermarkets. for example, the rule {onions, potatoes}=>{burger} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. 1 53 products NNS decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the items target value. it is one of the predictive modelling approaches used in statistics, data mining and machine learning. tree models where the target variable can take a finite set of values are called classification trees. in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. 1 53 finite JJ logistic regression was developed by statistician david cox in 1958[2][3](although much work was done in the single independent variable case almost two decades earlier). the binary logistic model is used to estimate the probability of a binary response based on one or more predictor (or independent) variables (features). as such it is not a classification method. it could be called a qualitative response/discrete choice model in the terminology of economics. 1 53 . O cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 54 main JJ in statistics, simple linear regression is the least squares estimator of a linear regression model with a single explanatory variable. in other words, simple linear regression fits a straight line through the set of n points in such a way that makes the sum of squared residuals of the model (that is, vertical distances between the points of the data set and the fitted line) as small as possible. 1 54 ( O association rule learning is a method for discovering interesting relations between variables in large databases. it is intended to identify strong rules discovered in databases using different measures of interestingness. based on the concept of strong rules, rakesh agrawal et al.[2] introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (pos) systems in supermarkets. for example, the rule {onions, potatoes}=>{burger} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. 1 54 in IN decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the items target value. it is one of the predictive modelling approaches used in statistics, data mining and machine learning. tree models where the target variable can take a finite set of values are called classification trees. in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. 1 54 set NN logistic regression was developed by statistician david cox in 1958[2][3](although much work was done in the single independent variable case almost two decades earlier). the binary logistic model is used to estimate the probability of a binary response based on one or more predictor (or independent) variables (features). as such it is not a classification method. it could be called a qualitative response/discrete choice model in the terminology of economics. 1 54 as IN cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 55 task NN in statistics, simple linear regression is the least squares estimator of a linear regression model with a single explanatory variable. in other words, simple linear regression fits a straight line through the set of n points in such a way that makes the sum of squared residuals of the model (that is, vertical distances between the points of the data set and the fitted line) as small as possible. 1 55 that WDT association rule learning is a method for discovering interesting relations between variables in large databases. it is intended to identify strong rules discovered in databases using different measures of interestingness. based on the concept of strong rules, rakesh agrawal et al.[2] introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (pos) systems in supermarkets. for example, the rule {onions, potatoes}=>{burger} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. 1 55 large-scale JJ decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the items target value. it is one of the predictive modelling approaches used in statistics, data mining and machine learning. tree models where the target variable can take a finite set of values are called classification trees. in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. 1 55 of IN logistic regression was developed by statistician david cox in 1958[2][3](although much work was done in the single independent variable case almost two decades earlier). the binary logistic model is used to estimate the probability of a binary response based on one or more predictor (or independent) variables (features). as such it is not a classification method. it could be called a qualitative response/discrete choice model in the terminology of economics. 1 55 such JJ in statistics, simple linear regression is the least squares estimator of a linear regression model with a single explanatory variable. in other words, simple linear regression fits a straight line through the set of n points in such a way that makes the sum of squared residuals of the model (that is, vertical distances between the points of the data set and the fitted line) as small as possible. 1 56 is VBZ cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 56 of IN decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the items target value. it is one of the predictive modelling approaches used in statistics, data mining and machine learning. tree models where the target variable can take a finite set of values are called classification trees. in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. 1 56 values NNS association rule learning is a method for discovering interesting relations between variables in large databases. it is intended to identify strong rules discovered in databases using different measures of interestingness. based on the concept of strong rules, rakesh agrawal et al.[2] introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (pos) systems in supermarkets. for example, the rule {onions, potatoes}=>{burger} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. 1 56 transaction NN logistic regression was developed by statistician david cox in 1958[2][3](although much work was done in the single independent variable case almost two decades earlier). the binary logistic model is used to estimate the probability of a binary response based on one or more predictor (or independent) variables (features). as such it is not a classification method. it could be called a qualitative response/discrete choice model in the terminology of economics. 1 56 it PRP logistic regression was developed by statistician david cox in 1958[2][3](although much work was done in the single independent variable case almost two decades earlier). the binary logistic model is used to estimate the probability of a binary response based on one or more predictor (or independent) variables (features). as such it is not a classification method. it could be called a qualitative response/discrete choice model in the terminology of economics. 1 57 is VBZ association rule learning is a method for discovering interesting relations between variables in large databases. it is intended to identify strong rules discovered in databases using different measures of interestingness. based on the concept of strong rules, rakesh agrawal et al.[2] introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (pos) systems in supermarkets. for example, the rule {onions, potatoes}=>{burger} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. 1 57 data NNS cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 57 exploratory JJ in statistics, simple linear regression is the least squares estimator of a linear regression model with a single explanatory variable. in other words, simple linear regression fits a straight line through the set of n points in such a way that makes the sum of squared residuals of the model (that is, vertical distances between the points of the data set and the fitted line) as small as possible. 1 57 , O decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the items target value. it is one of the predictive modelling approaches used in statistics, data mining and machine learning. tree models where the target variable can take a finite set of values are called classification trees. in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. 1 57 are VBP logistic regression was developed by statistician david cox in 1958[2][3](although much work was done in the single independent variable case almost two decades earlier). the binary logistic model is used to estimate the probability of a binary response based on one or more predictor (or independent) variables (features). as such it is not a classification method. it could be called a qualitative response/discrete choice model in the terminology of economics. 1 58 not RB in statistics, simple linear regression is the least squares estimator of a linear regression model with a single explanatory variable. in other words, simple linear regression fits a straight line through the set of n points in such a way that makes the sum of squared residuals of the model (that is, vertical distances between the points of the data set and the fitted line) as small as possible. 1 58 vertical JJ cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 58 data NN decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the items target value. it is one of the predictive modelling approaches used in statistics, data mining and machine learning. tree models where the target variable can take a finite set of values are called classification trees. in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. 1 58 called VBN association rule learning is a method for discovering interesting relations between variables in large databases. it is intended to identify strong rules discovered in databases using different measures of interestingness. based on the concept of strong rules, rakesh agrawal et al.[2] introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (pos) systems in supermarkets. for example, the rule {onions, potatoes}=>{burger} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. 1 58 recorded VBN logistic regression was developed by statistician david cox in 1958[2][3](although much work was done in the single independent variable case almost two decades earlier). the binary logistic model is used to estimate the probability of a binary response based on one or more predictor (or independent) variables (features). as such it is not a classification method. it could be called a qualitative response/discrete choice model in the terminology of economics. 1 59 a DT cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 59 mining NN in statistics, simple linear regression is the least squares estimator of a linear regression model with a single explanatory variable. in other words, simple linear regression fits a straight line through the set of n points in such a way that makes the sum of squared residuals of the model (that is, vertical distances between the points of the data set and the fitted line) as small as possible. 1 59 distances NNS association rule learning is a method for discovering interesting relations between variables in large databases. it is intended to identify strong rules discovered in databases using different measures of interestingness. based on the concept of strong rules, rakesh agrawal et al.[2] introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (pos) systems in supermarkets. for example, the rule {onions, potatoes}=>{burger} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. 1 59 by IN decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the items target value. it is one of the predictive modelling approaches used in statistics, data mining and machine learning. tree models where the target variable can take a finite set of values are called classification trees. in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. 1 59 classification NN decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the items target value. it is one of the predictive modelling approaches used in statistics, data mining and machine learning. tree models where the target variable can take a finite set of values are called classification trees. in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. 1 60 trees NNS in statistics, simple linear regression is the least squares estimator of a linear regression model with a single explanatory variable. in other words, simple linear regression fits a straight line through the set of n points in such a way that makes the sum of squared residuals of the model (that is, vertical distances between the points of the data set and the fitted line) as small as possible. 1 60 between IN cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 60 , O association rule learning is a method for discovering interesting relations between variables in large databases. it is intended to identify strong rules discovered in databases using different measures of interestingness. based on the concept of strong rules, rakesh agrawal et al.[2] introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (pos) systems in supermarkets. for example, the rule {onions, potatoes}=>{burger} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. 1 60 point-of-sale NN logistic regression was developed by statistician david cox in 1958[2][3](although much work was done in the single independent variable case almost two decades earlier). the binary logistic model is used to estimate the probability of a binary response based on one or more predictor (or independent) variables (features). as such it is not a classification method. it could be called a qualitative response/discrete choice model in the terminology of economics. 1 60 classification NN cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 61 and CC in statistics, simple linear regression is the least squares estimator of a linear regression model with a single explanatory variable. in other words, simple linear regression fits a straight line through the set of n points in such a way that makes the sum of squared residuals of the model (that is, vertical distances between the points of the data set and the fitted line) as small as possible. 1 61 the DT association rule learning is a method for discovering interesting relations between variables in large databases. it is intended to identify strong rules discovered in databases using different measures of interestingness. based on the concept of strong rules, rakesh agrawal et al.[2] introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (pos) systems in supermarkets. for example, the rule {onions, potatoes}=>{burger} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. 1 61 ( O decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the items target value. it is one of the predictive modelling approaches used in statistics, data mining and machine learning. tree models where the target variable can take a finite set of values are called classification trees. in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. 1 61 . O logistic regression was developed by statistician david cox in 1958[2][3](although much work was done in the single independent variable case almost two decades earlier). the binary logistic model is used to estimate the probability of a binary response based on one or more predictor (or independent) variables (features). as such it is not a classification method. it could be called a qualitative response/discrete choice model in the terminology of economics. 1 61 method NN cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 62 a DT in statistics, simple linear regression is the least squares estimator of a linear regression model with a single explanatory variable. in other words, simple linear regression fits a straight line through the set of n points in such a way that makes the sum of squared residuals of the model (that is, vertical distances between the points of the data set and the fitted line) as small as possible. 1 62 points NNS association rule learning is a method for discovering interesting relations between variables in large databases. it is intended to identify strong rules discovered in databases using different measures of interestingness. based on the concept of strong rules, rakesh agrawal et al.[2] introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (pos) systems in supermarkets. for example, the rule {onions, potatoes}=>{burger} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. 1 62 pos NNS decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the items target value. it is one of the predictive modelling approaches used in statistics, data mining and machine learning. tree models where the target variable can take a finite set of values are called classification trees. in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. 1 62 in IN logistic regression was developed by statistician david cox in 1958[2][3](although much work was done in the single independent variable case almost two decades earlier). the binary logistic model is used to estimate the probability of a binary response based on one or more predictor (or independent) variables (features). as such it is not a classification method. it could be called a qualitative response/discrete choice model in the terminology of economics. 1 62 . O cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 63 common JJ in statistics, simple linear regression is the least squares estimator of a linear regression model with a single explanatory variable. in other words, simple linear regression fits a straight line through the set of n points in such a way that makes the sum of squared residuals of the model (that is, vertical distances between the points of the data set and the fitted line) as small as possible. 1 63 of IN association rule learning is a method for discovering interesting relations between variables in large databases. it is intended to identify strong rules discovered in databases using different measures of interestingness. based on the concept of strong rules, rakesh agrawal et al.[2] introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (pos) systems in supermarkets. for example, the rule {onions, potatoes}=>{burger} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. 1 63 ) O decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the items target value. it is one of the predictive modelling approaches used in statistics, data mining and machine learning. tree models where the target variable can take a finite set of values are called classification trees. in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. 1 63 these DT logistic regression was developed by statistician david cox in 1958[2][3](although much work was done in the single independent variable case almost two decades earlier). the binary logistic model is used to estimate the probability of a binary response based on one or more predictor (or independent) variables (features). as such it is not a classification method. it could be called a qualitative response/discrete choice model in the terminology of economics. 1 63 it PRP in statistics, simple linear regression is the least squares estimator of a linear regression model with a single explanatory variable. in other words, simple linear regression fits a straight line through the set of n points in such a way that makes the sum of squared residuals of the model (that is, vertical distances between the points of the data set and the fitted line) as small as possible. 1 64 the DT decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the items target value. it is one of the predictive modelling approaches used in statistics, data mining and machine learning. tree models where the target variable can take a finite set of values are called classification trees. in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. 1 64 tree NN association rule learning is a method for discovering interesting relations between variables in large databases. it is intended to identify strong rules discovered in databases using different measures of interestingness. based on the concept of strong rules, rakesh agrawal et al.[2] introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (pos) systems in supermarkets. for example, the rule {onions, potatoes}=>{burger} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. 1 64 systems NNS logistic regression was developed by statistician david cox in 1958[2][3](although much work was done in the single independent variable case almost two decades earlier). the binary logistic model is used to estimate the probability of a binary response based on one or more predictor (or independent) variables (features). as such it is not a classification method. it could be called a qualitative response/discrete choice model in the terminology of economics. 1 64 could MD cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 64 technique NN in statistics, simple linear regression is the least squares estimator of a linear regression model with a single explanatory variable. in other words, simple linear regression fits a straight line through the set of n points in such a way that makes the sum of squared residuals of the model (that is, vertical distances between the points of the data set and the fitted line) as small as possible. 1 65 data NNS association rule learning is a method for discovering interesting relations between variables in large databases. it is intended to identify strong rules discovered in databases using different measures of interestingness. based on the concept of strong rules, rakesh agrawal et al.[2] introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (pos) systems in supermarkets. for example, the rule {onions, potatoes}=>{burger} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. 1 65 in IN decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the items target value. it is one of the predictive modelling approaches used in statistics, data mining and machine learning. tree models where the target variable can take a finite set of values are called classification trees. in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. 1 65 structures NNS logistic regression was developed by statistician david cox in 1958[2][3](although much work was done in the single independent variable case almost two decades earlier). the binary logistic model is used to estimate the probability of a binary response based on one or more predictor (or independent) variables (features). as such it is not a classification method. it could be called a qualitative response/discrete choice model in the terminology of economics. 1 65 be VB cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 65 for IN in statistics, simple linear regression is the least squares estimator of a linear regression model with a single explanatory variable. in other words, simple linear regression fits a straight line through the set of n points in such a way that makes the sum of squared residuals of the model (that is, vertical distances between the points of the data set and the fitted line) as small as possible. 1 66 set VBN association rule learning is a method for discovering interesting relations between variables in large databases. it is intended to identify strong rules discovered in databases using different measures of interestingness. based on the concept of strong rules, rakesh agrawal et al.[2] introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (pos) systems in supermarkets. for example, the rule {onions, potatoes}=>{burger} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. 1 66 supermarkets NNS decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the items target value. it is one of the predictive modelling approaches used in statistics, data mining and machine learning. tree models where the target variable can take a finite set of values are called classification trees. in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. 1 66 , O logistic regression was developed by statistician david cox in 1958[2][3](although much work was done in the single independent variable case almost two decades earlier). the binary logistic model is used to estimate the probability of a binary response based on one or more predictor (or independent) variables (features). as such it is not a classification method. it could be called a qualitative response/discrete choice model in the terminology of economics. 1 66 called VBN cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 66 statistical JJ in statistics, simple linear regression is the least squares estimator of a linear regression model with a single explanatory variable. in other words, simple linear regression fits a straight line through the set of n points in such a way that makes the sum of squared residuals of the model (that is, vertical distances between the points of the data set and the fitted line) as small as possible. 1 67 and CC association rule learning is a method for discovering interesting relations between variables in large databases. it is intended to identify strong rules discovered in databases using different measures of interestingness. based on the concept of strong rules, rakesh agrawal et al.[2] introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (pos) systems in supermarkets. for example, the rule {onions, potatoes}=>{burger} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. 1 67 . O decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the items target value. it is one of the predictive modelling approaches used in statistics, data mining and machine learning. tree models where the target variable can take a finite set of values are called classification trees. in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. 1 67 leaves VBZ logistic regression was developed by statistician david cox in 1958[2][3](although much work was done in the single independent variable case almost two decades earlier). the binary logistic model is used to estimate the probability of a binary response based on one or more predictor (or independent) variables (features). as such it is not a classification method. it could be called a qualitative response/discrete choice model in the terminology of economics. 1 67 a DT cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 67 data NNS in statistics, simple linear regression is the least squares estimator of a linear regression model with a single explanatory variable. in other words, simple linear regression fits a straight line through the set of n points in such a way that makes the sum of squared residuals of the model (that is, vertical distances between the points of the data set and the fitted line) as small as possible. 1 68 the DT association rule learning is a method for discovering interesting relations between variables in large databases. it is intended to identify strong rules discovered in databases using different measures of interestingness. based on the concept of strong rules, rakesh agrawal et al.[2] introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (pos) systems in supermarkets. for example, the rule {onions, potatoes}=>{burger} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. 1 68 for IN decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the items target value. it is one of the predictive modelling approaches used in statistics, data mining and machine learning. tree models where the target variable can take a finite set of values are called classification trees. in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. 1 68 represent JJ logistic regression was developed by statistician david cox in 1958[2][3](although much work was done in the single independent variable case almost two decades earlier). the binary logistic model is used to estimate the probability of a binary response based on one or more predictor (or independent) variables (features). as such it is not a classification method. it could be called a qualitative response/discrete choice model in the terminology of economics. 1 68 qualitative JJ cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 68 analysis NN in statistics, simple linear regression is the least squares estimator of a linear regression model with a single explanatory variable. in other words, simple linear regression fits a straight line through the set of n points in such a way that makes the sum of squared residuals of the model (that is, vertical distances between the points of the data set and the fitted line) as small as possible. 1 69 fitted JJ association rule learning is a method for discovering interesting relations between variables in large databases. it is intended to identify strong rules discovered in databases using different measures of interestingness. based on the concept of strong rules, rakesh agrawal et al.[2] introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (pos) systems in supermarkets. for example, the rule {onions, potatoes}=>{burger} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. 1 69 example NN decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the items target value. it is one of the predictive modelling approaches used in statistics, data mining and machine learning. tree models where the target variable can take a finite set of values are called classification trees. in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. 1 69 class NN logistic regression was developed by statistician david cox in 1958[2][3](although much work was done in the single independent variable case almost two decades earlier). the binary logistic model is used to estimate the probability of a binary response based on one or more predictor (or independent) variables (features). as such it is not a classification method. it could be called a qualitative response/discrete choice model in the terminology of economics. 1 69 response/discrete JJ cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 69 , O in statistics, simple linear regression is the least squares estimator of a linear regression model with a single explanatory variable. in other words, simple linear regression fits a straight line through the set of n points in such a way that makes the sum of squared residuals of the model (that is, vertical distances between the points of the data set and the fitted line) as small as possible. 1 70 line NN association rule learning is a method for discovering interesting relations between variables in large databases. it is intended to identify strong rules discovered in databases using different measures of interestingness. based on the concept of strong rules, rakesh agrawal et al.[2] introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (pos) systems in supermarkets. for example, the rule {onions, potatoes}=>{burger} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. 1 70 , O decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the items target value. it is one of the predictive modelling approaches used in statistics, data mining and machine learning. tree models where the target variable can take a finite set of values are called classification trees. in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. 1 70 labels NNS logistic regression was developed by statistician david cox in 1958[2][3](although much work was done in the single independent variable case almost two decades earlier). the binary logistic model is used to estimate the probability of a binary response based on one or more predictor (or independent) variables (features). as such it is not a classification method. it could be called a qualitative response/discrete choice model in the terminology of economics. 1 70 choice NN cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 70 used VBN in statistics, simple linear regression is the least squares estimator of a linear regression model with a single explanatory variable. in other words, simple linear regression fits a straight line through the set of n points in such a way that makes the sum of squared residuals of the model (that is, vertical distances between the points of the data set and the fitted line) as small as possible. 1 71 ) O association rule learning is a method for discovering interesting relations between variables in large databases. it is intended to identify strong rules discovered in databases using different measures of interestingness. based on the concept of strong rules, rakesh agrawal et al.[2] introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (pos) systems in supermarkets. for example, the rule {onions, potatoes}=>{burger} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. 1 71 the DT decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the items target value. it is one of the predictive modelling approaches used in statistics, data mining and machine learning. tree models where the target variable can take a finite set of values are called classification trees. in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. 1 71 and CC logistic regression was developed by statistician david cox in 1958[2][3](although much work was done in the single independent variable case almost two decades earlier). the binary logistic model is used to estimate the probability of a binary response based on one or more predictor (or independent) variables (features). as such it is not a classification method. it could be called a qualitative response/discrete choice model in the terminology of economics. 1 71 model NN cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 71 in IN in statistics, simple linear regression is the least squares estimator of a linear regression model with a single explanatory variable. in other words, simple linear regression fits a straight line through the set of n points in such a way that makes the sum of squared residuals of the model (that is, vertical distances between the points of the data set and the fitted line) as small as possible. 1 72 as RB association rule learning is a method for discovering interesting relations between variables in large databases. it is intended to identify strong rules discovered in databases using different measures of interestingness. based on the concept of strong rules, rakesh agrawal et al.[2] introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (pos) systems in supermarkets. for example, the rule {onions, potatoes}=>{burger} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. 1 72 rule NN decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the items target value. it is one of the predictive modelling approaches used in statistics, data mining and machine learning. tree models where the target variable can take a finite set of values are called classification trees. in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. 1 72 branches NNS logistic regression was developed by statistician david cox in 1958[2][3](although much work was done in the single independent variable case almost two decades earlier). the binary logistic model is used to estimate the probability of a binary response based on one or more predictor (or independent) variables (features). as such it is not a classification method. it could be called a qualitative response/discrete choice model in the terminology of economics. 1 72 in IN cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 72 many JJ in statistics, simple linear regression is the least squares estimator of a linear regression model with a single explanatory variable. in other words, simple linear regression fits a straight line through the set of n points in such a way that makes the sum of squared residuals of the model (that is, vertical distances between the points of the data set and the fitted line) as small as possible. 1 73 small JJ association rule learning is a method for discovering interesting relations between variables in large databases. it is intended to identify strong rules discovered in databases using different measures of interestingness. based on the concept of strong rules, rakesh agrawal et al.[2] introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (pos) systems in supermarkets. for example, the rule {onions, potatoes}=>{burger} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. 1 73 { O decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the items target value. it is one of the predictive modelling approaches used in statistics, data mining and machine learning. tree models where the target variable can take a finite set of values are called classification trees. in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. 1 73 represent VBP logistic regression was developed by statistician david cox in 1958[2][3](although much work was done in the single independent variable case almost two decades earlier). the binary logistic model is used to estimate the probability of a binary response based on one or more predictor (or independent) variables (features). as such it is not a classification method. it could be called a qualitative response/discrete choice model in the terminology of economics. 1 73 the DT cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 73 fields NNS in statistics, simple linear regression is the least squares estimator of a linear regression model with a single explanatory variable. in other words, simple linear regression fits a straight line through the set of n points in such a way that makes the sum of squared residuals of the model (that is, vertical distances between the points of the data set and the fitted line) as small as possible. 1 74 as IN association rule learning is a method for discovering interesting relations between variables in large databases. it is intended to identify strong rules discovered in databases using different measures of interestingness. based on the concept of strong rules, rakesh agrawal et al.[2] introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (pos) systems in supermarkets. for example, the rule {onions, potatoes}=>{burger} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. 1 74 onions NNS decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the items target value. it is one of the predictive modelling approaches used in statistics, data mining and machine learning. tree models where the target variable can take a finite set of values are called classification trees. in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. 1 74 conjunctions NNS logistic regression was developed by statistician david cox in 1958[2][3](although much work was done in the single independent variable case almost two decades earlier). the binary logistic model is used to estimate the probability of a binary response based on one or more predictor (or independent) variables (features). as such it is not a classification method. it could be called a qualitative response/discrete choice model in the terminology of economics. 1 74 terminology NN cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 74 , O in statistics, simple linear regression is the least squares estimator of a linear regression model with a single explanatory variable. in other words, simple linear regression fits a straight line through the set of n points in such a way that makes the sum of squared residuals of the model (that is, vertical distances between the points of the data set and the fitted line) as small as possible. 1 75 possible JJ association rule learning is a method for discovering interesting relations between variables in large databases. it is intended to identify strong rules discovered in databases using different measures of interestingness. based on the concept of strong rules, rakesh agrawal et al.[2] introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (pos) systems in supermarkets. for example, the rule {onions, potatoes}=>{burger} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. 1 75 , O decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the items target value. it is one of the predictive modelling approaches used in statistics, data mining and machine learning. tree models where the target variable can take a finite set of values are called classification trees. in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. 1 75 of IN logistic regression was developed by statistician david cox in 1958[2][3](although much work was done in the single independent variable case almost two decades earlier). the binary logistic model is used to estimate the probability of a binary response based on one or more predictor (or independent) variables (features). as such it is not a classification method. it could be called a qualitative response/discrete choice model in the terminology of economics. 1 75 of IN cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 75 including VBG in statistics, simple linear regression is the least squares estimator of a linear regression model with a single explanatory variable. in other words, simple linear regression fits a straight line through the set of n points in such a way that makes the sum of squared residuals of the model (that is, vertical distances between the points of the data set and the fitted line) as small as possible. 1 76 . O association rule learning is a method for discovering interesting relations between variables in large databases. it is intended to identify strong rules discovered in databases using different measures of interestingness. based on the concept of strong rules, rakesh agrawal et al.[2] introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (pos) systems in supermarkets. for example, the rule {onions, potatoes}=>{burger} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. 1 76 potatoes}=>{burger NN decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the items target value. it is one of the predictive modelling approaches used in statistics, data mining and machine learning. tree models where the target variable can take a finite set of values are called classification trees. in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. 1 76 features NNS logistic regression was developed by statistician david cox in 1958[2][3](although much work was done in the single independent variable case almost two decades earlier). the binary logistic model is used to estimate the probability of a binary response based on one or more predictor (or independent) variables (features). as such it is not a classification method. it could be called a qualitative response/discrete choice model in the terminology of economics. 1 76 economics NNS cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 76 machine NN association rule learning is a method for discovering interesting relations between variables in large databases. it is intended to identify strong rules discovered in databases using different measures of interestingness. based on the concept of strong rules, rakesh agrawal et al.[2] introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (pos) systems in supermarkets. for example, the rule {onions, potatoes}=>{burger} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. 1 77 } O decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the items target value. it is one of the predictive modelling approaches used in statistics, data mining and machine learning. tree models where the target variable can take a finite set of values are called classification trees. in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. 1 77 that WDT logistic regression was developed by statistician david cox in 1958[2][3](although much work was done in the single independent variable case almost two decades earlier). the binary logistic model is used to estimate the probability of a binary response based on one or more predictor (or independent) variables (features). as such it is not a classification method. it could be called a qualitative response/discrete choice model in the terminology of economics. 1 77 . O cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 77 learning NN association rule learning is a method for discovering interesting relations between variables in large databases. it is intended to identify strong rules discovered in databases using different measures of interestingness. based on the concept of strong rules, rakesh agrawal et al.[2] introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (pos) systems in supermarkets. for example, the rule {onions, potatoes}=>{burger} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. 1 78 found VBN decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the items target value. it is one of the predictive modelling approaches used in statistics, data mining and machine learning. tree models where the target variable can take a finite set of values are called classification trees. in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. 1 78 lead VBP cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 78 , O association rule learning is a method for discovering interesting relations between variables in large databases. it is intended to identify strong rules discovered in databases using different measures of interestingness. based on the concept of strong rules, rakesh agrawal et al.[2] introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (pos) systems in supermarkets. for example, the rule {onions, potatoes}=>{burger} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. 1 79 in IN decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the items target value. it is one of the predictive modelling approaches used in statistics, data mining and machine learning. tree models where the target variable can take a finite set of values are called classification trees. in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. 1 79 to TO cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 79 pattern JJ association rule learning is a method for discovering interesting relations between variables in large databases. it is intended to identify strong rules discovered in databases using different measures of interestingness. based on the concept of strong rules, rakesh agrawal et al.[2] introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (pos) systems in supermarkets. for example, the rule {onions, potatoes}=>{burger} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. 1 80 the DT decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the items target value. it is one of the predictive modelling approaches used in statistics, data mining and machine learning. tree models where the target variable can take a finite set of values are called classification trees. in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. 1 80 those DT cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 80 recognition NN association rule learning is a method for discovering interesting relations between variables in large databases. it is intended to identify strong rules discovered in databases using different measures of interestingness. based on the concept of strong rules, rakesh agrawal et al.[2] introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (pos) systems in supermarkets. for example, the rule {onions, potatoes}=>{burger} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. 1 81 sales NNS decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the items target value. it is one of the predictive modelling approaches used in statistics, data mining and machine learning. tree models where the target variable can take a finite set of values are called classification trees. in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. 1 81 class NN cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 81 , O association rule learning is a method for discovering interesting relations between variables in large databases. it is intended to identify strong rules discovered in databases using different measures of interestingness. based on the concept of strong rules, rakesh agrawal et al.[2] introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (pos) systems in supermarkets. for example, the rule {onions, potatoes}=>{burger} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. 1 82 data NN decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the items target value. it is one of the predictive modelling approaches used in statistics, data mining and machine learning. tree models where the target variable can take a finite set of values are called classification trees. in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. 1 82 labels NNS cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 82 image NN association rule learning is a method for discovering interesting relations between variables in large databases. it is intended to identify strong rules discovered in databases using different measures of interestingness. based on the concept of strong rules, rakesh agrawal et al.[2] introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (pos) systems in supermarkets. for example, the rule {onions, potatoes}=>{burger} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. 1 83 of IN decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the items target value. it is one of the predictive modelling approaches used in statistics, data mining and machine learning. tree models where the target variable can take a finite set of values are called classification trees. in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. 1 83 . O cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 83 analysis NN association rule learning is a method for discovering interesting relations between variables in large databases. it is intended to identify strong rules discovered in databases using different measures of interestingness. based on the concept of strong rules, rakesh agrawal et al.[2] introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (pos) systems in supermarkets. for example, the rule {onions, potatoes}=>{burger} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. 1 84 a DT decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the items target value. it is one of the predictive modelling approaches used in statistics, data mining and machine learning. tree models where the target variable can take a finite set of values are called classification trees. in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. 1 84 decision NN cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 84 , O association rule learning is a method for discovering interesting relations between variables in large databases. it is intended to identify strong rules discovered in databases using different measures of interestingness. based on the concept of strong rules, rakesh agrawal et al.[2] introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (pos) systems in supermarkets. for example, the rule {onions, potatoes}=>{burger} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. 1 85 supermarket NN decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the items target value. it is one of the predictive modelling approaches used in statistics, data mining and machine learning. tree models where the target variable can take a finite set of values are called classification trees. in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. 1 85 trees NNS cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 85 information NN association rule learning is a method for discovering interesting relations between variables in large databases. it is intended to identify strong rules discovered in databases using different measures of interestingness. based on the concept of strong rules, rakesh agrawal et al.[2] introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (pos) systems in supermarkets. for example, the rule {onions, potatoes}=>{burger} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. 1 86 would MD decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the items target value. it is one of the predictive modelling approaches used in statistics, data mining and machine learning. tree models where the target variable can take a finite set of values are called classification trees. in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. 1 86 where WRB cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 86 retrieval NN decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the items target value. it is one of the predictive modelling approaches used in statistics, data mining and machine learning. tree models where the target variable can take a finite set of values are called classification trees. in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. 1 87 the DT association rule learning is a method for discovering interesting relations between variables in large databases. it is intended to identify strong rules discovered in databases using different measures of interestingness. based on the concept of strong rules, rakesh agrawal et al.[2] introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (pos) systems in supermarkets. for example, the rule {onions, potatoes}=>{burger} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. 1 87 indicate VB cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 87 , O decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the items target value. it is one of the predictive modelling approaches used in statistics, data mining and machine learning. tree models where the target variable can take a finite set of values are called classification trees. in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. 1 88 target NN association rule learning is a method for discovering interesting relations between variables in large databases. it is intended to identify strong rules discovered in databases using different measures of interestingness. based on the concept of strong rules, rakesh agrawal et al.[2] introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (pos) systems in supermarkets. for example, the rule {onions, potatoes}=>{burger} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. 1 88 that IN cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 88 and CC cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 89 bioinformatics NNS association rule learning is a method for discovering interesting relations between variables in large databases. it is intended to identify strong rules discovered in databases using different measures of interestingness. based on the concept of strong rules, rakesh agrawal et al.[2] introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (pos) systems in supermarkets. for example, the rule {onions, potatoes}=>{burger} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. 1 89 if IN decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the items target value. it is one of the predictive modelling approaches used in statistics, data mining and machine learning. tree models where the target variable can take a finite set of values are called classification trees. in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. 1 89 variable NN cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 90 . O association rule learning is a method for discovering interesting relations between variables in large databases. it is intended to identify strong rules discovered in databases using different measures of interestingness. based on the concept of strong rules, rakesh agrawal et al.[2] introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (pos) systems in supermarkets. for example, the rule {onions, potatoes}=>{burger} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. 1 90 a DT decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the items target value. it is one of the predictive modelling approaches used in statistics, data mining and machine learning. tree models where the target variable can take a finite set of values are called classification trees. in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. 1 90 can MD association rule learning is a method for discovering interesting relations between variables in large databases. it is intended to identify strong rules discovered in databases using different measures of interestingness. based on the concept of strong rules, rakesh agrawal et al.[2] introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (pos) systems in supermarkets. for example, the rule {onions, potatoes}=>{burger} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. 1 91 customer NN decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the items target value. it is one of the predictive modelling approaches used in statistics, data mining and machine learning. tree models where the target variable can take a finite set of values are called classification trees. in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. 1 91 take VB cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 91 cluster NN association rule learning is a method for discovering interesting relations between variables in large databases. it is intended to identify strong rules discovered in databases using different measures of interestingness. based on the concept of strong rules, rakesh agrawal et al.[2] introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (pos) systems in supermarkets. for example, the rule {onions, potatoes}=>{burger} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. 1 92 buys VBZ decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the items target value. it is one of the predictive modelling approaches used in statistics, data mining and machine learning. tree models where the target variable can take a finite set of values are called classification trees. in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. 1 92 continuous JJ cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 92 analysis NN association rule learning is a method for discovering interesting relations between variables in large databases. it is intended to identify strong rules discovered in databases using different measures of interestingness. based on the concept of strong rules, rakesh agrawal et al.[2] introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (pos) systems in supermarkets. for example, the rule {onions, potatoes}=>{burger} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. 1 93 onions NNS decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the items target value. it is one of the predictive modelling approaches used in statistics, data mining and machine learning. tree models where the target variable can take a finite set of values are called classification trees. in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. 1 93 values NNS cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 93 itself PRP association rule learning is a method for discovering interesting relations between variables in large databases. it is intended to identify strong rules discovered in databases using different measures of interestingness. based on the concept of strong rules, rakesh agrawal et al.[2] introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (pos) systems in supermarkets. for example, the rule {onions, potatoes}=>{burger} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. 1 94 and CC decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the items target value. it is one of the predictive modelling approaches used in statistics, data mining and machine learning. tree models where the target variable can take a finite set of values are called classification trees. in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. 1 94 ( O cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 94 is VBZ association rule learning is a method for discovering interesting relations between variables in large databases. it is intended to identify strong rules discovered in databases using different measures of interestingness. based on the concept of strong rules, rakesh agrawal et al.[2] introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (pos) systems in supermarkets. for example, the rule {onions, potatoes}=>{burger} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. 1 95 potatoes VBZ decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the items target value. it is one of the predictive modelling approaches used in statistics, data mining and machine learning. tree models where the target variable can take a finite set of values are called classification trees. in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. 1 95 typically RB cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 95 not RB association rule learning is a method for discovering interesting relations between variables in large databases. it is intended to identify strong rules discovered in databases using different measures of interestingness. based on the concept of strong rules, rakesh agrawal et al.[2] introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (pos) systems in supermarkets. for example, the rule {onions, potatoes}=>{burger} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. 1 96 together RB decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the items target value. it is one of the predictive modelling approaches used in statistics, data mining and machine learning. tree models where the target variable can take a finite set of values are called classification trees. in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. 1 96 real JJ cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 96 one CD association rule learning is a method for discovering interesting relations between variables in large databases. it is intended to identify strong rules discovered in databases using different measures of interestingness. based on the concept of strong rules, rakesh agrawal et al.[2] introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (pos) systems in supermarkets. for example, the rule {onions, potatoes}=>{burger} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. 1 97 , O decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the items target value. it is one of the predictive modelling approaches used in statistics, data mining and machine learning. tree models where the target variable can take a finite set of values are called classification trees. in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. 1 97 numbers NNS cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 97 specific JJ association rule learning is a method for discovering interesting relations between variables in large databases. it is intended to identify strong rules discovered in databases using different measures of interestingness. based on the concept of strong rules, rakesh agrawal et al.[2] introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (pos) systems in supermarkets. for example, the rule {onions, potatoes}=>{burger} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. 1 98 they PRP decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the items target value. it is one of the predictive modelling approaches used in statistics, data mining and machine learning. tree models where the target variable can take a finite set of values are called classification trees. in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. 1 98 ) O cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 98 algorithm NN association rule learning is a method for discovering interesting relations between variables in large databases. it is intended to identify strong rules discovered in databases using different measures of interestingness. based on the concept of strong rules, rakesh agrawal et al.[2] introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (pos) systems in supermarkets. for example, the rule {onions, potatoes}=>{burger} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. 1 99 are VBP decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the items target value. it is one of the predictive modelling approaches used in statistics, data mining and machine learning. tree models where the target variable can take a finite set of values are called classification trees. in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. 1 99 are VBP cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 99 , O association rule learning is a method for discovering interesting relations between variables in large databases. it is intended to identify strong rules discovered in databases using different measures of interestingness. based on the concept of strong rules, rakesh agrawal et al.[2] introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (pos) systems in supermarkets. for example, the rule {onions, potatoes}=>{burger} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. 1 100 likely JJ decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the items target value. it is one of the predictive modelling approaches used in statistics, data mining and machine learning. tree models where the target variable can take a finite set of values are called classification trees. in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. 1 100 called VBN cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 100 but CC association rule learning is a method for discovering interesting relations between variables in large databases. it is intended to identify strong rules discovered in databases using different measures of interestingness. based on the concept of strong rules, rakesh agrawal et al.[2] introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (pos) systems in supermarkets. for example, the rule {onions, potatoes}=>{burger} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. 1 101 to TO decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the items target value. it is one of the predictive modelling approaches used in statistics, data mining and machine learning. tree models where the target variable can take a finite set of values are called classification trees. in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. 1 101 regression NN cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 101 the DT association rule learning is a method for discovering interesting relations between variables in large databases. it is intended to identify strong rules discovered in databases using different measures of interestingness. based on the concept of strong rules, rakesh agrawal et al.[2] introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (pos) systems in supermarkets. for example, the rule {onions, potatoes}=>{burger} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. 1 102 also RB decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the items target value. it is one of the predictive modelling approaches used in statistics, data mining and machine learning. tree models where the target variable can take a finite set of values are called classification trees. in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. 1 102 trees NNS cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 102 general JJ association rule learning is a method for discovering interesting relations between variables in large databases. it is intended to identify strong rules discovered in databases using different measures of interestingness. based on the concept of strong rules, rakesh agrawal et al.[2] introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (pos) systems in supermarkets. for example, the rule {onions, potatoes}=>{burger} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. 1 103 buy VB decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the items target value. it is one of the predictive modelling approaches used in statistics, data mining and machine learning. tree models where the target variable can take a finite set of values are called classification trees. in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. 1 103 . O cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 103 task NN association rule learning is a method for discovering interesting relations between variables in large databases. it is intended to identify strong rules discovered in databases using different measures of interestingness. based on the concept of strong rules, rakesh agrawal et al.[2] introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (pos) systems in supermarkets. for example, the rule {onions, potatoes}=>{burger} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. 1 104 hamburger JJR cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 104 to TO association rule learning is a method for discovering interesting relations between variables in large databases. it is intended to identify strong rules discovered in databases using different measures of interestingness. based on the concept of strong rules, rakesh agrawal et al.[2] introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (pos) systems in supermarkets. for example, the rule {onions, potatoes}=>{burger} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. 1 105 meat NN cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 105 be VB association rule learning is a method for discovering interesting relations between variables in large databases. it is intended to identify strong rules discovered in databases using different measures of interestingness. based on the concept of strong rules, rakesh agrawal et al.[2] introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (pos) systems in supermarkets. for example, the rule {onions, potatoes}=>{burger} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. 1 106 . O cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 106 solved VBN cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 107 . O cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 108 it PRP cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 109 can MD cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 110 be VB cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 111 achieved VBN cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 112 by IN cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 113 various JJ cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 114 algorithms NNS cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 115 that WDT cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 116 differ VBP cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 117 significantly RB cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 118 in IN cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 119 their PRP$ cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 120 notion NN cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 121 of IN cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 122 what WP cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 123 constitutes VBZ cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 124 a DT cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 125 cluster NN cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 126 and CC cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 127 how WRB cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 128 to TO cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 129 efficiently RB cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 130 find VB cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 131 them PRP cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). it is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. cluster analysis itself is not one specific algorithm, but the general task to be solved. it can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 132 . O

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