A New Fuzzy Co-clustering Algorithm for Categorization of Datasets with Overlapping Clusters

Fuzzy co-clustering is a method that performs simultaneous fuzzy clustering of objects and features. In this paper, we introduce a new fuzzy coclustering algorithm for high-dimensional datasets called Cosine-Distancebased & Dual-partitioning Fuzzy Co-clustering (CODIALING FCC). Unlike many existing fuzzy co-clustering algorithms, CODIALING FCC is a dualpartitioning algorithm. It clusters the features in the same manner as it clusters the objects, that is, by partitioning them according to their natural groupings. It is also a cosine-distance-based algorithm because it utilizes the cosine distance to capture the belongingness of objects and features in the co-clusters. Our main purpose of introducing this new algorithm is to improve the performance of some prominent existing fuzzy co-clustering algorithms in dealing with datasets with high overlaps. In our opinion, this is very crucial since most real-world datasets involve significant amount of overlaps in their inherent clustering structures. We discuss how this improvement can be made through the dualpartitioning formulation adopted. Experimental results on a toy problem and five large benchmark document datasets demonstrate the effectiveness of CODIALING FCC in handling overlaps better.