The maximum descent (MD) algorithms have been proposed for clustering objects. Compared with the traditional K-means (or GLA, or LBG) algorithm, the MD algorithms achieve better clustering performance with far less computation time. However, the searching of the optimal partitioning hyperplane of a multidimensional cluster is a difficult problem in the MD algorithms. In this paper, a new partition technique based on tabu search (TS) approach is presented for the MD algorithms. Experimental results show that the tabu search based MD algorithm can produce a better clustering performance than the K-means and MD algorithms.
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