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

Paper:

# 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
Accepted:
September 23, 2017
Published:
January 20, 2018
Keywords:
fuzzy clustering, relational data, q-divergence
Abstract

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.

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