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Activity Number: 39 - Topics in Clustering
Type: Contributed
Date/Time: Sunday, July 29, 2018 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistical Learning and Data Science
Abstract #330001 Presentation
Title: Hierarchical Significance Testing for Gaussian Mixture Clustering
Author(s): Purvasha Chakravarti* and Larry Wasserman and Sivaraman Balakrishnan
Companies: Carnegie Mellon University and Carnegie Mellon University and Carnegie Mellon University
Keywords: Clustering; Hierarchical; Hypothesis Testing; Significance clustering

Liu, Hayes, Nobel and Marron (2008) introduced a hypothesis testing method (SigClust) for deciding whether a Gaussian should be split into two clusters. When applied recursively, this defines a method for hierarchical clustering that comes equipped with a significance guarantee. In this paper, we improve on this procedure. First we study the power of SigClust and we show that the method can have very low power. We define a new test, RIFT (Relative Information Fit Test), that has higher power. Then we show that the method provably finds the correct clustering structure under certain conditions.

Authors who are presenting talks have a * after their name.

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