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Activity Number: 485
Type: Invited
Date/Time: Wednesday, August 1, 2012 : 10:30 AM to 12:20 PM
Sponsor: SSC
Abstract - #303792
Title: Clustering and Biclustering of Relational Data
Author(s): Alejandro Murua*+ and Thierry Chekouo Tekougang
Companies: Université de Montréal and Université de Montréal
Address: , , ,
Keywords: clustering ; biclustering ; auto-logistic model ; Bayesian plaid model ; gene expression ; deviance information criterion

In many applications the data points interdependence may be complex. We suppose that this can be modeled through a relational graph. The strength of the dependence between points is given by the graph edge weights. For example, when analyzing gene expression data, the genes interdependence may be given by their common ontologies. The data clustering or biclustering must obey the constraints of the point relationships. In this work, we introduce a biclustering model that takes into account the data point relationships. These are modeled by imposing an auto-logistic prior on the vertices of the relational graph. The biclustering model is given by a Bayesian Plaid-type model. We also present a modified DIC criterion to choose the appropriate number of biclusters. We show some applications of our ideas to gene expression data. We note that very few statistical models for biclustering have been proposed in the literature. Instead, most of the research has focused on algorithms to find biclusters.

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