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Deterministic Annealing Framework in MMMs-Induced Fuzzy Co-Clustering and Its Applicability


Shunnya Oshio, Katsuhiro Honda, Seiki Ubukata, Akira Notsu


Vol. 16  No. 1  pp. 43-50


Initialization problem is a significant issue in FCM-type clustering models, in which alternative optimization is often started with random initial partitions and can be trapped into local optima caused by 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, the initialization sensitivity issue is considered in multinomial mixture models-induced fuzzy co-clustering context and a new approach for implementing the deterministic annealing mechanism to fuzzy co-clustering is proposed. The advantages of the proposed approach against the conventional statistical co-clustering model are demonstrated through some numerical experiments.


Fuzzy co-clustering, Multinomial mixture, Deterministic annealing, Initialization problem.