JACIII Vol.16 No.7 pp. 784-792
doi: 10.20965/jaciii.2012.p0784


Entropy-Regularized Fuzzy Clustering for Non-Euclidean Relational Data and Indefinite Kernel Data

Yuchi Kanzawa

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

May 20, 2012
September 22, 2012
November 20, 2012
entropy-regularized fuzzy c-means, non-Euclidean relational data, indefinite kernel
In this paper, an entropy-regularized fuzzy clustering approach for non-Euclidean relational data and indefinite kernel data is developed that has not previously been discussed. It is important because relational data and kernel data are not always Euclidean and positive semi-definite, respectively. It is theoretically determined that an entropy-regularized approach for both non-Euclidean relational data and indefinite kernel data can be applied without using a β-spread transformation, and that two other options make the clustering results crisp for both data types. These results are in contrast to those from the standard approach. Numerical experiments are employed to verify the theoretical results, and the clustering accuracy of three entropy-regularized approaches for non-Euclidean relational data, and three for indefinite kernel data, is compared.
Cite this article as:
Y. Kanzawa, “Entropy-Regularized Fuzzy Clustering for Non-Euclidean Relational Data and Indefinite Kernel Data,” J. Adv. Comput. Intell. Intell. Inform., Vol.16 No.7, pp. 784-792, 2012.
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