A kernel prototype-based clustering algorithm

Authors:
Chi-Yuan Yeh;Shie-Jue Lee;Chih-Hung Wu;Shing H. Doong
Affiliations:
Department of Electrical Engineering, National Sun Yat-Sen University, Kaohsiung, Taiwan;Department of Electrical Engineering, National Sun Yat-Sen University, Kaohsiung, Taiwan;Department of Electrical Engineering, National University of Kaohsiung, Kaohsiung, Taiwan;Department of Information Management, ShuTe University, Taiwan
Venue:
ICCOMP'06 Proceedings of the 10th WSEAS international conference on Computers
Year:
2006

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Abstract

One-class SVM is a kernel-based method which utilizes the kernel trick for data clustering. However it is only able to detect one cluster of non-convex shape. In this study, we propose a strategy using one-class SVM to calculate the centroid of the sphere for each cluster in feature space. In addition, a mechanism is provided to control the position of the cluster centroid in feature space to work against outliers. We compare our method with other kernel prototype-based clustering algorithms, like KKM and KFCM, on two synthetic data sets and four UCI real data sets, the results indicate that our method outperforms KKM and KFCM.