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

Mayetri Gupta

Computational Statistics & Data Analysis, 2014, vol. 71, issue C, 375-391

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.

Keywords: Biclustering; Markov chain Monte Carlo; Bayesian modeling; Genome-wide association studies (search for similar items in EconPapers)
Date: 2014
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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:71:y:2014:i:c:p:375-391

DOI: 10.1016/j.csda.2013.07.006

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