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Activity Number:
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393
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Type:
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Invited
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Date/Time:
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Wednesday, August 9, 2006 : 10:30 AM to 12:20 PM
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Sponsor:
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WNAR
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| Abstract - #304897 |
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Title:
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Variable Selection in Clustering via Dirichlet Process Mixture Models
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Author(s):
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Marina Vannucci*+
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Companies:
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Texas A&M University
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Address:
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, College Station, TX, 77843-3143,
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Keywords:
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clustering ; Dirichlet process mixture ; microarrays
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Abstract:
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The increased collection of high-dimensional data in various fields has raised a strong interest in clustering algorithms and variable selection procedures. In this talk, a model-based method that handles the two problems simultaneously will be proposed. Dirichlet process mixture models are used to define the cluster structure, and a latent binary vector is introduced to identify discriminating variables. Inference on the cluster structure is done via Gibbs sampling, and the variable selection index is updated using stochastic search techniques. Performance of the methodology is explored on simulated and DNA microarray data.
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- The address information is for the authors that have a + after their name.
- Authors who are presenting talks have a * after their name.
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