Cluster Validation in Multinomial Mixtures-Induced Fuzzy Co-Clustering
Katsuhiro Honda, Yurina Suzuki, Seiki Ubukata, Akira Notsu · 2016
Cluster validation is an important process in FCM-type clustering, where the optimal cluster partition should be selected from candidate solutions derived with various initialization and cluster numbers. In the standard FCM clustering, some validity measures such as partition quality-based or geometric features-based ones have been proposed. In this paper, fuzzy cluster validation is discussed in the multinomial mixtures-induced fuzzy co-clustering context. Partition quality-based indices are directly applied to the prototype-less co-clustering model with a brief modification. The concept of a geometric-based index is achieved supported by the object-item aggregation measure. The characteristics of the proposed indices are demonstrated in numerical experiments.