Extension of fuzzy c-means algorithm

被引：0
|
作者：
Li, CJ [1 ]
Becerra, VM [1 ]
Deng, JM [1 ]
机构：
[1] Hangzhou Dianzi Univ, Sch Sci, Hangzhou 310018, Peoples R China
来源：
2004 IEEE CONFERENCE ON CYBERNETICS AND INTELLIGENT SYSTEMS, VOLS 1 AND 2 | 2004年
关键词：
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：
Clustering is a procedure through which objects are distinguished or classified in accordance with their similarity. The Fuzzy c-Means method (FCM) is one of the most popular clustering methods based on minimization of a criterion function. However, the FCM method is sensitive to the presence of noise and outliers in data. This paper introduces a new clustering algorithm by extending the criterion function. As a special case, this algorithm includes the well-known Fuzzy c-Means method. Performance of the new clustering algorithm is experimentally compared with the FCM method using synthetic data with different clusters and outliers.
引用
收藏
页码：405 / 409
页数：5
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