A new fuzzy C-means with priority

被引：0
|
作者：
Gao, Jun [1 ]
Xiang, Lili [1 ]
Wang, Jiandong [2 ]
机构：
[1] Yancheng Inst Technol, Jiangsu, Peoples R China
[2] Nanjing Univ Aeronaut & Astronaut, Nanjing, Jiangsu, Peoples R China
来源：
PROCEEDINGS OF 2007 IEEE INTERNATIONAL CONFERENCE ON GREY SYSTEMS AND INTELLIGENT SERVICES, VOLS 1 AND 2 | 2007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：
As traditional fuzzy C-means (FCM) has its shortages, we present an improved algorithm: M_FCM. The theory of fuzzy equivalency is used to deal with the original samples for getting the number of clusters and the original clustering center. By improving the clustering objective functions, the abilities of C-means are to handle isolated points and to show the significance of each dimension in the samples for clustering effect also enhanced. An experiment is finally conducted to make the M_FCM clearer.
引用
收藏
页码：1014 / 1018
页数：5
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