Robust kernel fuzzy clustering

被引：0
|
作者：
Du, WW [1 ]
Inoue, K [1 ]
Urahama, K [1 ]
机构：
[1] Kyushu Univ, Fukuoka 8158540, Japan
来源：
FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY, PT 1, PROCEEDINGS | 2005年 / 3613卷
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：
We present a method for extracting arbitrarily shaped clusters buried in uniform noise data. The popular k-means algorithm is firstly fuzzified with addition of entropic terms to the objective function of data partitioning problem. This fuzzy clustering is then kernelized for adapting to the arbitrary shape of clusters. Finally, the Euclidean distance in this kernelized fuzzy clustering is modified to a robust one for avoiding the influence of noisy background data. This robust kernel fuzzy clustering method is shown to outperform every its predecessor: fuzzified k-means, robust fuzzified k-means and kernel fuzzified k-means algorithms.
引用
收藏
页码：454 / 461
页数：8
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