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JACIII Vol.23 No.3 pp. 485-492
doi: 10.20965/jaciii.2019.p0485
(2019)

Paper:

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Regularized Fuzzy c-Means Clustering and its Behavior at Point of Infinity

Yuchi Kanzawa* and Sadaaki Miyamoto**

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

**University of Tsukuba
1-1-1 Tennodai, Tsukuba, Ibaraki 305-8573, Japan

Received:
January 9, 2018
Accepted:
December 5, 2018
Published:
May 20, 2019
Creative Commons License
Keywords:
fuzzy clustering, regularization
Abstract

This study shows that a general regularized fuzzy c-means (rFCM) clustering algorithm, including some conventional clustering algorithms, can be constructed if a given regularizer function value, its derivative function value, and its inverse derivative function value can be calculated. Furthermore, the results of the study show that the behavior of the fuzzy classification function for rFCM at an infinity point is similar to that for some conventional clustering algorithms.

Fuzzy classification function of regularized fuzzy c-means clustering

Fuzzy classification function of regularized fuzzy c-means clustering

Cite this article as:
Y. Kanzawa and S. Miyamoto, “Regularized Fuzzy c-Means Clustering and its Behavior at Point of Infinity,” J. Adv. Comput. Intell. Intell. Inform., Vol.23 No.3, pp. 485-492, 2019.
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Last updated on Apr. 22, 2024