A New Validity Function For Fuzzy Clustering

被引：19
作者：
Li, Yang [1 ]
Yu, Fusheng [1 ]
机构：
[1] Beijing Normal Univ, Sch Math Sci, Lab Math & Complex Syst, Minist Educ, Beijing 100875, Peoples R China
来源：
PROCEEDINGS OF THE 2009 INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND NATURAL COMPUTING, VOL I | 2009年
关键词：
Fuzzy C-Means; fuzzy clustering analysis; cluster number; clustering validity function;
D O I：
10.1109/CINC.2009.100
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper first gives a new validity function for fuzzy clustering, then presents a method of the optimal selecting of the cluster number in the standard fuzzy c-means clustering algorithm, and finally outlines the fuzzy c-means clustering algorithm with parameters self-adapted. Experimental results carried on synthetic data set and data set based on actual background illustrate the performance of the new validity function and the corresponding fuzzy clustering algorithm.
引用
收藏
页码：462 / 465
页数：4
相关论文
共 14 条
[1]  
[Anonymous], Pattern Recognition with Fuzzy Objective Function Algorithms
[2]  
de Oliverira J. V., 2007, ADV FUZZY CLUSTERING
[3]  
Gao X., 1999, DEV FUZZY CLUSTERING
[4]  
Gong G. Y., 2004, STUDY FCM ALGORITHMS
[5]  
HE Q, 1998, THEORY FUZZY CLUSTER
[6]  
HU BQ, 2004, BASIC FUZZY THEORY
[7]  
KRISHNAPURAM R, 1995, IEEE T FUZZY SYST, V3, P29, DOI 10.1109/91.366564
[8]  
LI RP, 1995, MAXIMUM ENTROPY APPR, P2227
[9]  
Luo C.Z., 2005, Introduction to Fuzzy Sets
[10]  
MANTARAS RLA, 1998, IEEE T PAMI, V10, P754
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