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Biometrika 2006 93(4):877-893; doi:10.1093/biomet/93.4.877
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© 2006 Biometrika Trust

Variable selection in clustering via Dirichlet process mixture models

Sinae Kim1, Mahlet G. Tadesse2 and Marina Vannucci3

1 Department of Statistics, Texas A&M University, College Station, Texas 77843-3143, U.S.A. sinae{at}stat.tamu.edu, 2 Department of Biostatistics and Epidemiology, University of Pennsylvania, Philadelphia, Pennsylvania 19104-6021, U.S.A. mtadesse{at}cceb.upenn.edu, 3 Department of Statistics, Texas A&M University, College Station, Texas 77843-3143, U.S.A. mvannucci{at}stat.tamu.edu


  Abstract

The increased collection of high-dimensional data in various fields has raised a strong interest in clustering algorithms and variable selection procedures. In this paper, we propose a model-based method that addresses the two problems simultaneously. We introduce a latent binary vector to identify discriminating variables and use Dirichlet process mixture models to define the cluster structure. We update the variable selection index using a Metropolis algorithm and obtain inference on the cluster structure via a split-merge Markov chain Monte Carlo technique. We explore the performance of the methodology on simulated data and illustrate an application with a DNA microarray study.

Key Words: Bayesian inference; Clustering; Dirichlet process mixture model; DNA microarray data analysis; Variable selection.

Received December 2004. Revised March 2006.


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