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An evolutionary Monte Carlo algorithm for Bayesian block clustering of data matrices

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  • Gupta, Mayetri

Abstract

In many applications, it is of interest to simultaneously cluster row and column variables in a data set, identifying local subgroups within a data matrix that share some common characteristic. When a small set of variables is believed to be associated with a set of responses, block clustering or biclustering is a more appropriate technique to use compared to one-dimensional clustering. A flexible framework for Bayesian model-based block clustering, that can determine multiple block clusters in a data matrix through a novel and efficient evolutionary Monte Carlo-based methodology, is proposed. The performance of this methodology is illustrated through a number of simulation studies and an application to data from genome-wide association studies.

Suggested Citation

  • Gupta, Mayetri, 2014. "An evolutionary Monte Carlo algorithm for Bayesian block clustering of data matrices," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 375-391.
    • Handle: RePEc:eee:csdana:v:71:y:2014:i:c:p:375-391
    DOI: 10.1016/j.csda.2013.07.006
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    References listed on IDEAS

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