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- Title
Variable selection in clustering via Dirichlet process mixture models.
- Authors
Kim, Sinae; Tadesse, Mahlet G.; Vannucci, Marina
- 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.
- Subjects
DIRICHLET series; ALGORITHMS; MONTE Carlo method; MARKOV processes; DNA microarrays
- Publication
Biometrika, 2006, Vol 93, Issue 4, p877
- ISSN
0006-3444
- Publication type
Article
- DOI
10.1093/biomet/93.4.877