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JACIII Vol.25 No.1 pp. 73-82
doi: 10.20965/jaciii.2021.p0073
(2021)

Paper:

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Generalized Fuzzy c-Means Clustering and its Property of Fuzzy Classification Function

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:
March 16, 2020
Accepted:
November 4, 2020
Published:
January 20, 2021
Creative Commons License
Keywords:
fuzzy c-means clustering, fuzzy classification function
Abstract

This study shows that a generalized fuzzy c-means (gFCM) clustering algorithm, which covers both standard and exponential fuzzy c-means clustering, can be constructed if a given fuzzified function, its derivative, and its inverse derivative can be calculated. Furthermore, our results show that the fuzzy classification function for gFCM exhibits a behavior similar to that of both standard and exponential fuzzy c-means clustering.

FCF example of Generalized FCM

FCF example of Generalized FCM

Cite this article as:
Y. Kanzawa and S. Miyamoto, “Generalized Fuzzy c-Means Clustering and its Property of Fuzzy Classification Function,” J. Adv. Comput. Intell. Intell. Inform., Vol.25 No.1, pp. 73-82, 2021.
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Last updated on Apr. 19, 2024