7.00.02 - Cluster Analysis - Aster Analytics

Teradata Aster® Analytics Foundation User GuideUpdate 2

Product
Aster Analytics
Release Number
7.00.02
Published
September 2017
Content Type
Programming Reference
User Guide
Publication ID
B700-1022-700K
Language
English (United States)
Last Update
2018-04-17
Cluster Analysis Functions
Function Description
Canopy Simple, fast, accurate function for grouping objects into preliminary clusters. Often used as an initial step in more rigorous clustering techniques, such as k-means.
Gaussian Mixture Model Functions Fit a Gaussian mixture model (GMM) to input data, using either a basic GMM algorithm with a fixed number of clusters or a Dirichlet Process GMM (DP-GMM) algorithm with a variable number of clusters. The GMM functions are GMMFit, GMMPredict, and GMMProfile.
KMeans Takes a data set and outputs the centroids of its clusters and, optionally, the clusters themselves.
KMeansPlot Takes a model—a table of cluster centroids output by the KMeans function—and an input table of test data, and uses the model to assign the test data points to the cluster centroids.
KModes Extends KMeans to support categorical data. The core algorithm is an expectation-maximization algorithm that finds a locally optimal solution.
KModesPredict Prediction function that corresponds to KModes.
Minhash Probabilistic clustering method that assigns a pair of users to the same cluster with probability proportional to the overlap between the sets of items that these users have bought.