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Abstract Details
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Activity Number:
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341
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Type:
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Contributed
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Date/Time:
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Tuesday, August 2, 2011 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Statistical Computing
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| Abstract - #303004 |
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Title:
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Probabilistic Biclustering
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Author(s):
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Thao Duong*+ and Hal Stern and Irvine Curtis Deutsch
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Companies:
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University of California at Irvine and University of California at Irvine and University of Massachusetts
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Address:
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, , ,
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Keywords:
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data mining ;
biclustering ;
algorithms ;
bayesian ;
mcmc ;
mixture model
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Abstract:
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Biclustering refers to identification of subsets of individuals (units) and subsets of measurements (features) that define interesting partitions of the data. We formulate a parameter probability model-based biclustering approach. We handle the problem of identifying the discriminative features and the observations in each cluster by introducing binary latent vectors (for rows and columns seperately) for each cluster. The resulting method selects the optimal number of clusters, discriminating features and observations in each group simultaneously. We apply the method to simulated and real examples.
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