Analytic Functions - Analytics Database - Teradata Vantage

These analytic functions are new to this release:

• TD_Pivoting. Pivot the data, that is, changes the data from sparse to dense format.
• TD_Unpivoting. Unpivots the data, that is, changes the data from dense format to sparse format.

These analytic functions were enhanced in this release:

• TD_CategoricalSummary. DistinctValue column supports VARCHAR (CHARACTER SET LATIN or UNICODE) data type in the output table schema.
• TD_DecisionForestPredict. Performance and usability enhancements like changing accumulate columns at the end of the output.
• TD_GLMPredict. Optional Family argument to TD_GLMPredict. The argument specifies the distribution exponential family that was used with TD_GLM to train the model.
• TD_KMeans. Supports KMeans++ algorithm for initial centroids selection. KMeans++ algorithm is a way for choosing initial centroids far away from each other and reduces the possibility of initial centroids being chosen from the same cluster. KMeans++ improves the overall quality of clustering and can also speed up the convergence of KMeans algorithm.
• TD_KMeansPredict. Takes a table of cluster centroids output by the TD_KMeans function and an input table. It uses the model to assign the input data points to the cluster centroids.
• TD_Scalefit and TD_ScaleTransform. Supports PARTITION BY along with ParameterTable and AttributeTable to scale different input data partitions independently of each other. Added an optional argument IgnoreInvalidLocationScale that gives you an option to ignore errors for invalid values of location and scale parameters.
• TD_SVMPredict. Optional ModelType argument added to TD_SVMPredict. The argument specifies the model type used by TD_SVM to train the dataset.

See Teradata Vantage™ - Analytics Database Analytic Functions.