document: A generalized maximum entropy approach to bregman co-clustering and matrix approximation Co-clustering is a powerful data mining technique with varied applications such as text clustering , microarray analysis and recommender systems . Recently , an information-theoretic co-clustering approach applicable to empirical joint probability distributions was proposed . In many situations , co-clustering of more general matrices is desired . In this paper , we present a substantially generalized co-clustering framework wherein any Bregman divergence can be used in the objective function , and various conditional expectation based constraints can be considered based on the statistics that need to be preserved . Analysis of the co-clustering problem leads to the minimum Bregman information principle , which generalizes the maximum entropy principle , and yields an elegant meta algorithm that is guaranteed to achieve local optimality . Our methodology yields new algorithms and also encompasses several previously known clustering and co-clustering algorithms based on alternate minimization .
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