Modified possibilistic clustering model based on kernel methods

A novel model of fuzzy clustering using kernel methods is proposed. This model is called kernel modified possibilistic c-means （KMPCM） model. The proposed model is an extension of the modified possibilistic c-means （MPCM） algorithm by using kernel methods. Different from MPCM and fuzzy c-means （FCM）...

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Bibliographic Details
Published inJournal of Shanghai University Vol. 12; no. 2; pp. 136 - 140
Main Author 武小红 周建江
Format Journal Article
LanguageEnglish
Published Heidelberg Shanghai University Press 01.04.2008
Subjects
Classical Mechanics
Engineering
Environment
Life Sciences
Materials Science
Mathematical and Computational Engineering
Mechatronics
可能聚类模型
改进方法
核方法
计算机技术
kernel methods
possibilistic c-means (PCM)
fuzzy clustering
kernel modified possibilistic c-means (KMPCM)
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Summary:A novel model of fuzzy clustering using kernel methods is proposed. This model is called kernel modified possibilistic c-means （KMPCM） model. The proposed model is an extension of the modified possibilistic c-means （MPCM） algorithm by using kernel methods. Different from MPCM and fuzzy c-means （FCM） model which are based on Euclidean distance, the proposed model is based on kernel-induced distance. Furthermore, with kernel methods the input data can be mapped implicitly into a high-dimensional feature space where the nonlinear pattern now appears linear. It is unnecessary to do calculation in the high-dimensional feature space because the kernel function can do it. Numerical experiments show that KMPCM outperforms FCM and MPCM.
Bibliography:TP1
31-1735/N
fuzzy clustering, kernel methods, possibilistic c-means （PCM）, kernel modified possibilistic c-means （KMPCM）.
ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:1007-6417
1863-236X
DOI:10.1007/s11741-008-0210-2