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

被引：8

作者：

Kanzawa, Yuchi ^{[1
]}

Miyamoto, Sadaaki ^{[2
]}

机构：

[1] Shibaura Inst Technol, Koto Ku, 3-7-5 Toyosu, Tokyo 1358548, Japan

[2] Univ Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 3058573, Japan

来源：

JOURNAL OF ADVANCED COMPUTATIONAL INTELLIGENCE AND INTELLIGENT INFORMATICS | 2021年 / 25卷 / 01期

关键词：

fuzzy c-means clustering; fuzzy classification function; MAXIMIZING MODEL; ALGORITHMS;

D O I：

10.20965/jaciii.2021.p0073

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：73 / 82

页数：10

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