Cluster Analysis - Teradata Vantage - Short descriptions of cluster analysis functions, with links to their documentation

Machine Learning Engine Analytic Function Reference

Product
Teradata Vantage
Release Number
8.00
1.0
Published
May 2019
Language
English (United States)
Last Update
2019-11-22
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B700-4003
lifecycle
previous
Product Category
Teradata Vantageā„¢
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.
KMeans Functions Create and use model that is table of cluster centroids. Optionally output clusters themselves.
KModes Functions Extends KMeans functions to support categorical data.
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.