Possibilistic Fuzzy c-Means Clustering Model Using Kernel Methods

Authors:
Xiao-Hong Wu;Jian-Jiang Zhou
Affiliations:
Jiangsu University, Zhenjiang, China;Nanjing University of Aeronautics and Astronautics, Nanjing, China
Venue:
CIMCA '05 Proceedings of the International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce Vol-2 (CIMCA-IAWTIC'06) - Volume 02
Year:
2005

Citing 0
Cited 2

Possibilistic Clustering in Feature Space

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High dimensional data clustering through fuzzy possibilistic C-means with symmetry-based distance measure

International Journal of Computational Intelligence Studies

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Abstract

A fuzzy clustering method is presented based on kernel methods. The proposed model is called kernel possibilistic fuzzy c-means model (KPFCM). It is claimed that KPFCM is an extension of possibilistic fuzzy c-means model (PFCM) which is superior to fuzzy c-means (FCM) model. Different from PFCM and FCM which are based on Euclidean distance, the proposed model is based on non-Euclidean distance by using kernel methods. Furthermore, with kernel methods the input data can be mapped implicitly into a high-dimensional feature space where the nonlinear pattern now appears linear. KPFCM can deal with noises or outliers better than PFCM. The proposed model is interesting and provides good solution. The experimental results show better performance of KPFCM.