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

被引:10
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
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
相关论文
共 21 条
[1]  
Bezdek J. C., 1981, Pattern recognition with fuzzy objective function algorithms
[2]   Concept decompositions for large sparse text data using clustering [J].
Dhillon, IS ;
Modha, DS .
MACHINE LEARNING, 2001, 42 (1-2) :143-175
[3]  
Honda K, 2014, 2014 IEEE INTERNATIONAL CONFERENCE ON GRANULAR COMPUTING (GRC), P94, DOI 10.1109/GRC.2014.6982814
[4]  
Honda K, 2014, IEEE INT FUZZY SYST, P2505, DOI 10.1109/FUZZ-IEEE.2014.6891747
[5]  
Ichihashi H., 2000, P 4 AS FUZZ SYST S, P217
[6]  
Kanzawa Y., 2015, LNCS, V9321, P125
[7]  
Kanzawa Y, 2015, J ADV COMPUT INTELL, V19, P852
[8]  
Kanzawa Y, 2015, J ADV COMPUT INTELL, V19, P738
[9]  
Kanzawa Y, 2015, J ADV COMPUT INTELL, V19, P662