# Output - Aster Analytics

## Teradata AsterĀ® Analytics Foundation User GuideUpdate 2

Product
Aster Analytics
Release Number
7.00.02
Published
September 2017
Language
English (United States)
Last Update
2018-04-17
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uce1497542673292.ditamap
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dita:id
B700-1022
lifecycle
previous
Product Category
Software
Sentenizer Example Output Table
paraid paratopic sentence_sn sentence
1 Decision Trees 1 Decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the items target value.
1 Decision Trees 2 It is one of the predictive modelling approaches used in statistics, data mining and machine learning.
1 Decision Trees 3 Tree models where the target variable can take a finite set of values are called classification trees.
1 Decision Trees 4 In these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels.
1 Decision 5 Decision trees where the target variable can take continuous values (typically real numbers) are called regression trees.
2 Simple Regression 1 In statistics, simple linear regression is the least squares estimator of a linear regression model with a single explanatory variable.
2 Simple Regression 2 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.
3 Logistic Regression 1 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).
3 Logistic Regression 2 The binary logistic model is used to estimate the probability of a binary response based on one or more predictor (or independent) variables (features).
3 Logistic Regression 3 As such it is not a classification method.
3 Logistic Regression 4 It could be called a qualitative response/discrete choice model in the terminology of economics.
4 Cluster analysis 1 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).
4 Cluster analysis 2 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.
4 Cluster analysis 3 Cluster analysis itself is not one specific algorithm, but the general task to be solved.
4 Cluster analysis 4 It can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them.
5 Association rule learning 1 Association rule learning is a method for discovering interesting relations between variables in large databases.
5 Association rule learning 2 It is intended to identify strong rules discovered in databases using different measures of interestingness.
5 Association rule learning 3 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.
5 Association rule learning 4 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.