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In high dimensional data space, clusters are likely to exist in different subspaces. K-means is a classic clustering algorithm, but it cannot be used to find subspace clusters. In this paper, an algorithm called GKM is designed to generalize k-means algorithm for high dimensional data. In the objective function of GKM, we associate a weight vector with each cluster to indicate which dimensions are...
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