A Noise Fuzzy Co-Clustering Scheme in MMMs-Induced Clustering
Katsuhiro Honda, Nami Yamamoto, Seiki Ubukata, Akira Notsu · 2016
Noise fuzzy clustering is a practical model for handling noisy data sets in FCM clustering, where an additional noise cluster is introduced for dumping noise objects into it. Because the noise cluster is assumed to have an equal (fixed) distance from all objects, noise objects having larger distances from all clusters can be assigned to the noise cluster. In this paper, a novel scheme for implementing noise clustering in fuzzy coclustering is proposed, where noise cluster is defined in a slightly different manner from the FCM-type one because co-clustering is a prototype-less clustering. A noise co-cluster is defined with homogeneous item memberships for drawing noise objects, whose cooccurrence features are dissimilar from all general clusters. The characteristics of the proposed scheme are demonstrated in numerical experiments.