Nonparametric Bayesian Co-clustering Ensembles

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Nonparametric Bayesian Co-clustering Ensembles
A nonparametric Bayesian approach to co-clustering ensembles is presented. Similar to clustering ensembles, coclustering ensembles combine various base co-clustering results to obtain a more robust consensus co-clustering. To avoid pre-specifying the number of co-clusters, we specify independent Dirichlet process priors for the row and column clusters. Thus, the numbers of row- and column-clusters are unbounded a priori; the actual numbers of clusters can be learned a posteriori from observations. Next, to model non-independence of row- and column-clusters, we employ a Mondrian Process as a prior distribution over partitions of the data matrix. As a result, the co-clusters are not restricted to a regular grid partition, but form nested partitions with varying resolutions. The empirical evaluation demonstrates the effectiveness of nonparametric Bayesian co-clustering ensembles and their advantages over traditional co-clustering methods.
Pu Wang, Kathryn B. Laskey, Carlotta Domeniconi, M
Added 17 Sep 2011
Updated 17 Sep 2011
Type Journal
Year 2011
Where SDM
Authors Pu Wang, Kathryn B. Laskey, Carlotta Domeniconi, Michael Jordan
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