Co-Clustering with Generative Models

Abstract

In this paper, we present a generative model for co-clustering and develop algorithms based on the mean field approximation for the corresponding modeling problem. These algorithms can be viewed as generalizations of the traditional model-based clustering; they extend hard co-clustering algorithms such as Bregman co-clustering to include soft assignments. We show empirically that these model-based algorithms offer better performance than their hard-assignment counterparts, especially with increasing problem complexity.