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 ...

• Type: Conference
• Year: 2003
• Where: KDD
• Authors: Inderjit S. Dhillon, Subramanyam Mallela, Dharmendra S. Modha
• Keywords: Contingency Table Analysis, Data Mining, KDD 2003, Simultaneous Clustering, Two-dimensional Contingency