JACIII Vol.23 No.3 pp. 561-570
doi: 10.20965/jaciii.2019.p0561


Fuzzy Clustering Method for Spherical Data Based on q-Divergence

Masayuki Higashi, Tadafumi Kondo, and Yuchi Kanzawa

Shibaura Institute of Technology
3-7-5 Toyosu, Koto, Tokyo 135-8548, Japan

January 9, 2018
January 16, 2019
May 20, 2019
fuzzy clustering, spherical data, KL-divergence, q-divergence

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.

Fuzzy clustering for spherical data

Fuzzy clustering for spherical data

Cite this article as:
M. Higashi, T. Kondo, and Y. Kanzawa, “Fuzzy Clustering Method for Spherical Data Based on q-Divergence,” J. Adv. Comput. Intell. Intell. Inform., Vol.23 No.3, pp. 561-570, 2019.
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