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