JACIII Vol.22 No.1 pp. 34-43
doi: 10.20965/jaciii.2018.p0034


q-Divergence-Based Relational Fuzzy c-Means Clustering

Yuchi Kanzawa

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

November 20, 2016
September 23, 2017
January 20, 2018
fuzzy clustering, relational data, q-divergence

In this paper, a clustering algorithm for relational data based on q-divergence between memberships and variables that control cluster sizes is proposed. A conventional method for vectorial data is first presented for interpretation as the regularization of another conventional method with q-divergence. With this interpretation, a clustering algorithm for relational data, based on q-divergence, is then derived from an optimization problem built by regularizing the conventional method with q-divergence. A theoretical discussion reveals the property of the proposed method. Numerical results are presented that substantiate this property and show that the proposed method outperforms two conventional methods in terms of accuracy.

Cite this article as:
Y. Kanzawa, “q-Divergence-Based Relational Fuzzy c-Means Clustering,” J. Adv. Comput. Intell. Intell. Inform., Vol.22 No.1, pp. 34-43, 2018.
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