Auto-Selection of Cluster Number in MMMs-Induced Fuzzy Co-Clustering

Abstract

Fuzzy co-clustering induced by multinomial mixture models (FCCMM) is an effective method for analyzing such cooccurrence information data as document-keyword frequencies, but often suffers from the cluster validation problem due to a priori selection of cluster numbers. In this paper, a modified model of robust cluster number selection in Gaussian mixture models is proposed, where the optimal number of clusters are automatically extracted in FCCMM through rejection of unnecessary clusters considering a novel penalty on cluster volumes.