An immune-inspired evolutionary fuzzy clustering algorithm based on constrained optimization

被引：0
|
作者：
Liu, Li [1 ]
Xu, Wenbo [1 ]
机构：
[1] Southern Yangtze Univ, Sch Informat Technol, Wuxi 214122, JiangSu, Peoples R China
来源：
ISDA 2006: SIXTH INTERNATIONAL CONFERENCE ON INTELLIGENT SYSTEMS DESIGN AND APPLICATIONS, VOL 1 | 2006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：
It is possible to view the clustering problem as an optimization problem that locates the optimal centroids of the clusters directly under the membership function constraints, rather than finding an optimal partition. In this paper a new evolutionary approach to fuzzy clustering is introduced, which based on the application of artificial immune principles to the fuzzy c-means clustering algorithm. The theoretical aspects as well as experimental results are presented. The convergence and parameter setting are also discussed.
引用
收藏
页码：966 / 970
页数：5
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