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The K-means algorithm is widely used in vector quantizer (VQ) design and clustering analysis. In VQ context, this algorithm iteratively updates an initial codebook and converges to a locally optimal codebook in certain conditions. It iteratively satisfies each of the two necessary conditions for an optimal quantizer; the nearest neighbor condition for the partition and centroid condition for the codevectors. In this letter, we propose a new algorithm for both vector quantizer design and clustering analysis as an alternative to the conventional K-means algorithm. The algorithm is almost the same as the K-means algorithm except for a modification at codebook updating step. It does not satisfy the centroid condition iteratively, but asymptotically satisfies it as the number of iterations increases. Experimental results show that the algorithm converges to a better locally optimal codebook with an accelerated convergence speed.