document: Information-theoretic co-clustering Two-dimensional contingency or co-occurrence tables arise frequently in important applications such as text , web-log and market-basket data analysis . A basic problem in contingency table analysis is co-clustering : simultaneous clustering of the rows and columns . A novel theoretical formulation views the contingency table as an empirical joint probability distribution of two discrete random variables and poses the co-clustering problem as an optimization problem in information theory -- the optimal co-clustering maximizes the mutual information between the clustered random variables subject to constraints on the number of row and column clusters . We present an innovative co-clustering algorithm that monotonically increases the preserved mutual information by intertwining both the row and column clusterings at all stages . Using the practical example of simultaneous word-document clustering , we demonstrate that our algorithm works well in practice , especially in the presence of sparsity and high-dimensionality .
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