Dual Exclusive Partition in Fuzzy CoDoK and SCAD-based Fuzzy Co-clustering
Chi-Hyon Oh, Katsuhiro Honda · Procedia Computer Science · 2013
Fuzzy co-clustering is a basic technique for revealing intrinsic co-cluster structures from cooccurrence information among objects and items. In most of fuzzy co-clustering algorithms, objects and items are partitioned based on different constraints. Objects are forced to be exclusively partitioned like as Fuzzy c-Means (FCM), while item memberships often represent just the relative significance of items in each cluster, i.e., items can be shared by multiple clusters. In a previous work, exclusive partition of items were achieved by introducing a penalty term in Fuzzy Clustering for Categorical Multivariate data (FCCM), which is an FCM-type co-clustering with entropy regularization mechanism. In this paper, the applicability of dual exclusive partition of objects and items are discussed in the frameworks of Fuzzy CoDoK and SCAD-based fuzzy co-clustering.