Normalized possibilistic clustering algorithms

被引:0
|
作者
Zhou, Jian [1 ]
Hung, Chih-Cheng [1 ]
He, Jing [1 ]
Luo, Yuxing [1 ]
机构
[1] Tsinghua Univ, Dept Ind Engn, Beijing 100084, Peoples R China
来源
PROCEEDINGS OF THE SIXTH INTERNATIONAL CONFERENCE ON INFORMATION AND MANAGEMENT SCIENCES | 2007年 / 6卷
关键词
fuzzy clustering; possibilistic clustering; possibility theory; fuzzy set theory;
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
A generalized approach to possibilistic clustering algorithms was proposed in [17], where the memberships are evaluated directly according to the data information using the fuzzy set theory, and the cluster centers are updated via a performance index. The computational experiments in[171 based on the generalized possibilistic clustering algorithms revealed that these clustering algorithms could not provide very stable results when clustering some data sets. Following that, a new performance index with possibility weights is used to obtain a new update equation for the cluster centers, which lead to the normalized possibilistic clustering algorithms. The performance and efficiency of a specific normalized possibilistic clustering algorithm are illustrated by numerical experiments based. A comparison with the generalized possibilistic clustering algorithms shows that the new algorithm could present very stable clustering results.
引用
收藏
页码:397 / 403
页数:7
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