Variable selection in clustering via Dirichlet process mixture models

Sinae Kim, Mahlet G. Tadesse and Marina Vannucci

Biometrika, 2006, vol. 93, issue 4, 877-893

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. Copyright 2006, Oxford University Press.

Date: 2006
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