Multi-regularization for fuzzy co-clustering

Co-clustering is a powerful technique with varied applications in text clustering and recommender systems. For large scale high dimensional and sparse real world data, there is a strong need to provide an overlapped co-clustering algorithm that mitigates the effect of noise and non-discriminative information, generalizes well to the unseen data, and performs well with respect to several quality measures. In this paper, we introduce a novel fuzzy co-clustering algorithm that incorporates multiple regularizers to address these important issues. Specifically, we propose MRegFC that considers terms corresponding to Entropy, Gini Index, and Joint Entropy simultaneously. We demonstrate that MRegFC generates significantly higher quality results compared to many existing approaches on several real world benchmark datasets.