Fuzzy self-organizing map based on regularized fuzzy c-means clustering

被引：0
|
作者：
Abonyi, J [1 ]
Migaly, S [1 ]
Szeifert, F [1 ]
机构：
[1] Univ Veszprem, Dept Proc Engn, H-8201 Veszprem, Hungary
来源：
ADVANCES IN SOFT COMPUTING: ENGINEERING DESIGN AND MANUFACTURING | 2003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：
This paper presents a new fuzzy clustering algorithm for the clustering and visualization of high-dimensional data. The cluster centers are arranged on a grid defined on a small dimensional space that can be easily visualized. The smoothness of this mapping is achieved by adding a regularization term to the fuzzy c-means (FCM) functional. The measure of the smoothness is expressed as the sum of the second order partial derivatives of the cluster centers. Coding the values of the cluster centers with colors, regions with different colors evolve on the map and the hidden relation between the variables reveal. Comparison to the existing modifications of the fuzzy c-means algorithm and several application examples are given.
引用
收藏
页码：99 / 108
页数：10
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