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.
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Oshio, S., Honda, K., Ubukata, S., Notsu, A. (2015). A Deterministic Clustering Framework in MMMs-Induced Fuzzy Co-clustering. In: Huynh, VN., Inuiguchi, M., Demoeux, T. (eds) Integrated Uncertainty in Knowledge Modelling and Decision Making. IUKM 2015. Lecture Notes in Computer Science(), vol 9376. Springer, Cham. https://doi.org/10.1007/978-3-319-25135-6_20
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DOI: https://doi.org/10.1007/978-3-319-25135-6_20
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