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Two Phase Implementation of MMMs-induced Fuzzy Co-clustering with Partially Exclusive Item Assignment


Takaya Nakano, Katsuhiro Honda, Seiki Ubukata, Akira Notsu


Vol. 17  No. 1  pp. 67-72


Fuzzy co-clustering is a basic tool for extracting pair-wise clusters of familiar objects and items from cooccurrence information. A promising improvement of the conventional fuzzy co-clustering algorithms is achieved by introducing exclusive nature to item partition with the goal of the improvement of interpretability of co-clusters. However, in practice, some items are quite popular and to be shared by multiple clusters, and only a selected part of items should be exclusively assigned to unique clusters. In this paper, a partially exclusive item partition model is introduced into multinomial mixture models-induced fuzzy co-clustering and a two phase implementation is proposed for determining the optimal set of items to be exclusively assigned. Its characteristic features are demonstrated through a numerical experiment with a real-world benchmark data set.


Fuzzy clustering, Co-clustering, Multinomial mixture, Exclusive partition, Classification.