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Data clustering is a hot problem and has been studied extensively. In this paper, we propose a novel support vector and K-Means based hybrid algorithm for data clustering. Firstly, we identify the outliers and overlapping data points through the support vector approach. Secondly, we remove the outliers and overlapping data points and then run the K-Means on the rest data points to obtain clustered data set. Finally, we build support vector description for each cluster, and then assign the removed data points to the cluster with the smallest distance, thus resulting in labeling the whole data set. Simulation results demonstrate that the proposed algorithm is effective, which exploits the advantages of both support vector clustering and K-Means.