Fuzzy Clustering Method for Spherical Data Based on q-Divergence
Masayuki Higashi, Tadafumi Kondo, Yuchi Kanzawa · Journal of Advanced Computational Intelligence and Intelligent Informatics · 2019
This study presents a fuzzy clustering algorithm for classifying spherical data based on q-divergence. First, it is shown that a conventional method for vectorial data is equivalent to the regularization of another conventional method using q-divergence. Next, based on the knowledge that q-divergence is a generalization of Kullback-Leibler (KL)-divergence and that there is a conventional fuzzy clustering method for classifying spherical data based on KL-divergence, a fuzzy clustering algorithm for spherical data is derived based on q-divergence. This algorithm uses an optimization problem built by extending KL-divergence in the conventional method to q-divergence. Finally, some numerical experiments are conducted to verify the proposed methods.