An alternative fuzzy compactness and separation clustering algorithm

被引：0

作者：

Yang, MS ^{[1
]}

Tsai, HS

机构：

[1] Chung Yuan Christian Univ, Dept Appl Math, Chungli 32023, Taiwan

[2] Takming Coll, Dept Management Informat Syst, Taipei 11451, Taiwan

来源：

ADVANCED CONCEPTS FOR INTELLIGENT VISION SYSTEMS, PROCEEDINGS | 2005年 / 3708卷

关键词：

fuzzy clustering algorithms; fuzzy c-means (FCM); fuzzy compactness & separation (FCS); alternative fuzzy compactness & separation (AFCS); exponential-type distance; robust; noise;

D O I：

暂无

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 presents a fuzzy clustering algorithm, called an alternative fuzzy compactness & separation (AFCS) algorithm that is based on an exponential-type distance function. The proposed AFCS algorithm is more robust than the fuzzy c-means (FCM) and the fuzzy compactness & separation (FCS) proposed by Wu et al. (2005). Some numerical experiments are performed to assess the performance of FCM, FCS and AFCS algorithms. Numerical results show that the AFCS has better performance than the FCM and FCS from the robust point of view.

引用

页码：146 / 153

页数：8

共 14 条

[1]

[Anonymous], Pattern Recognition With Fuzzy Objective Function Algorithms

[2]

Baraldi A, 1999, IEEE T SYST MAN CY B, V29, P778, DOI 10.1109/3477.809032

[3]

Duda R. O., 1973, PATTERN CLASSIFICATI

[4]

GATH J, 1989, IEEE T PATTERN ANAL, V11, P773

[5]

Gustafson D. E., 1979, Proceedings of the 1978 IEEE Conference on Decision and Control Including the 17th Symposium on Adaptive Processes, P761

[6] Generalized fuzzy c-means clustering strategies using Lp norm distances [J].

Hathaway, RJ ;

Bezdek, JC ;

Hu, YK .

IEEE TRANSACTIONS ON FUZZY SYSTEMS, 2000, 8 (05) :576-582

[7]

Hoppner F., 1999, FUZZY CLUSTER ANAL M

[8] Clustering algorithms based on volume criteria [J].

Krishnapuram, R ;

Kim, J .

IEEE TRANSACTIONS ON FUZZY SYSTEMS, 2000, 8 (02) :228-236

[9] A fuzzy algorithm for color quantization of images [J].

Özdemir, D ;

Akarun, L .

PATTERN RECOGNITION, 2002, 35 (08) :1785-1791

[10] A novel fuzzy clustering algorithm based on a fuzzy scatter matrix with optimality tests [J].

Wu, KL ;

Yu, J ;

Yang, MS .

PATTERN RECOGNITION LETTERS, 2005, 26 (05) :639-652

← 1 2 →