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

Paper:

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
Keywords:
fuzzy c-means clustering, fuzzy classification function
Abstract
Generalized Fuzzy <b><i>c</i></b>-Means Clustering and its Property of Fuzzy Classification Function

FCF example of Generalized FCM

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.

Cite this article as:
Yuchi Kanzawa and Sadaaki 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.
Data files:
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Last updated on Mar. 05, 2021