Consideration of Graded Possibilistic Approach in MMMs-induced Fuzzy Co-clustering

Abstract

In the field of fuzzy clustering, possibilistic clustering approaches that generate possibilistic partitions by eliminating probabilistic constraints have been reported to be effective for analyzing noisy data. Additionally, graded possibilistic approaches that gradually relax the probabilistic constraints and realize transition between probabilistic and possibilistic partition have been proposed for utilizing advantages of probabilistic and possibilistic clustering. Fuzzy co-clustering is a promising approach for analyzing cooccurrence information. In this paper, we propose a graded possibilistic approach for fuzzy co-clustering that realizes transition between probabilistic and possibilistic partition. Furthermore, we confirm characteristics and performances of the proposed method through numerical experiments.