Regularization of Fuzzy Clustering for Cooccurrence Matrix by K-L Information

Abstract

In this study, we propose a graded possibilistic approach to fuzzy clustering for cooccurrence matrix (FCCM) using regularization technique with Kullback-Leibler divergences (K-L information). FCCM partitions individuals and items of the cooccurrence matrix by maximizing the degree of aggregation of each cluster. In FCCM, when the number of items is large, the values of memberships for them will become small and make it difficult to interpret the absolute responsibility of them. By applying the graded possibilistic approach using regularization with K-L information to FCCM, we can make the absolute responsibility of items clear and handle the cluster capacity.