![]() ![]() Notsu, “Fuzzy co-clustering induced by multinomial mixture models,” J. Honda, “Fuzzy Collaborative Forecasting and Clustering,” Springer, 2019. Krishnapuram, “Fuzzy co-clustering of documents and keywords,” Proc. of Joint 9th IFSA World Congress and 20th NAFIPS Int. Ichihashi, “Fuzzy clustering for categorical multivariate data,” Proc. Honda, “Algorithms for Fuzzy Clustering,” Springer, 2008. Bezdek, “Pattern Recognition with Fuzzy Objective Function Algorithms,” Plenum Press, 1981. Data files: BIB TEX (BibDesk, LaTeX) RIS (EndNote, Reference Manager, etc) TEXT References ![]() Notsu, “A Heuristic-Based Model for MMMs-Induced Fuzzy Co-Clustering with Dual Exclusive Partition,” J. In this paper, a heuristic-based approach in FCM-type co-clustering is modified for adopting in MMMs-induced fuzzy co-clustering and its characteristics are demonstrated through several comparative experiments.įull text (0.6MB) (free) Cite this article as: K. In previous studies, the item sharing penalty approach has been applied to the MMMs-induced model but the dual exclusive constraints approach has not yet. In order to improve the interpretability of co-clusters, meaningful items should not belong to multiple clusters such that each co-cluster is characterized by different representative items. Because memberships of objects and items are usually estimated under different constraints, the conventional models mainly targeted object clusters only, but item memberships were designed for representing intra-cluster typicalities of items, which are independently estimated in each cluster. MMMs-induced fuzzy co-clustering achieves dual partition of objects and items by estimating two different types of fuzzy memberships. Co-clustering of cooccurrence information ![]()
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