On Some Fuzzy Clustering Algorithms based on Multivariate Power Exponential Distributions

Abstract

Fuzzy clustering algorithms that extend mixture models of Gaussian and t-distributions have been proposed under various fuzzification. On the other hand, multivariate power exponential distribution has been applied only to probabilistic mixture models and has not been extended to fuzzy clustering. In this report, we propose five fuzzy clustering algorithms that extend the mixture model based on multivariate power exponential distribution by following KL-divergence regularization type, Fuzzy Classification Maximum Likelihood, Tsallis-entropy regularization type, q-divergence regularization type, and Bezdek type. The effectiveness of the proposed algorithms is verified by numerical experiments.