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Abstract Details

Activity Number: 402
Type: Contributed
Date/Time: Tuesday, July 31, 2012 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistical Learning and Data Mining
Abstract - #305681
Title: Cluster Analysis via Random Partition Distributions
Author(s): Bryce Little and David Dahl*+ and Mahlet Tadesse
Companies: Texas A&M University and Brigham Young University and Georgetown University
Address: Department of Statistics, Provo, 84602, United States
Keywords: Hierarchical clustering ; Random partitions ; Pairwise distances ; Clustering methods ; Bayesian nonparametrics
Abstract:

We present a suite of clustering algorithms based on random partition distributions indexed by pairwise distance information.  Having their roots from the Bayesian nonparametric literature, these proposals are interesting in that they use the same input data as does hierarchical clustering yet are based on formal probability distribution.  Hence, the resulting clusterings can be justified by theory and statements about clustering uncertainty can be readily addressed.  We find empirically that our proposals are competitive with traditional clustering methods across a variety of clustering scenarios and are superior in select cases.


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