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Erschienen in:
2015 | OriginalPaper | Buchkapitel
A Deterministic Clustering Framework in MMMs-Induced Fuzzy Co-clustering
verfasst von : Shunnya Oshio, Katsuhiro Honda, Seiki Ubukata, Akira Notsu
Erschienen in: Integrated Uncertainty in Knowledge Modelling and Decision Making
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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.