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In the paper, we applied the idea of kernel-based learning methods to K-means clustering. We propose a fast and effective algorithm of kernel K-means clustering. The idea of the algorithm is that we firstly map the data from their original space to a high dimensional space (or kernel space) where the data are expected to be more separable. Then we perform K-means clustering in the high dimensional kernel space. Meanwhile we improve speed of the algorithm by using a new kernel function-conditionally positive definite kernel (CPD). The performance of new algorithm has been demonstrated to be superior to that of K-means clustering algorithm by our experiments on artificial and real data.