A kernel prototype-based clustering algorithm

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.

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