Flexible Fuzzy Co-clustering with Feature-cluster Weighting

Fuzzy co-clustering is an unsupervised technique that performs simultaneous fuzzy clustering of objects and features. In this paper, we propose a new flexible fuzzy co-clustering algorithm which incorporates feature-cluster weighting in the formulation. We call it Flexible Fuzzy Co-clustering with Feature-cluster Weighting (FFCFW). By flexible we mean the algorithm allows the number of object clusters to be different from the number of feature clusters. There are two motivations behind this work. First, in the fuzzy framework, many co-clustering algorithms still require the number of object clusters to be the same as the number of feature clusters. This is despite the fact that such rigid structure is hardly found in real-world applications. The second motivation is that while there have been numerous attempts for flexible co-clustering, it is common that in such scheme the relationships between object and feature clusters are not clearly represented. For this reason we incorporate a feature-cluster weighting scheme for each object cluster generated by FFCFW so that the relationships between the two types of clusters are manifested in the feature-cluster weights. This enables the new algorithm to generate more accurate representation of fuzzy co-clusters. FFCFW is formulated by fusing together the core components of two existing algorithms. Like its predecessors, FFCFW adopts an iterative optimization procedure. We discuss in details the derivation of the proposed algorithm and the advantages it has over other existing works. Experiments on several large benchmark document datasets reveal the feasibility of our proposed algorithm