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

被引：9
|
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
收藏
页码：73 / 82
页数：10
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