A deterministic clustering framework in MMMs-induced fuzzy co-clustering

Abstract

Although various FCM-type clustering models are utilized in many unsupervised classification tasks, they often suffer from bad initialization. The deterministic clustering approach is a practical procedure for utilizing a robust feature of very fuzzy partitions and tries to converge the iterative FCM process to a plausible solution by gradually decreasing the fuzziness degree. In this paper, a novel framework for implementing the deterministic annealing mechanism to fuzzy co-clustering is proposed. The advantages of the proposed framework against the conventional statistical co-clustering model are demonstrated through some numerical experiments.