Automatic subspace clustering of high dimensional data for data mining applications
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The widely used K-means clustering deals with ball-shaped (spherical Gaussian) clusters. In this paper, we extend the K-means clustering to accommodate extended clusters in subspaces, such as line-shaped clusters, plane-shaped clusters, and ball-shaped clusters. The algorithm retains much of the K-means clustering flavors: easy to implement and fast to converge. A model selection procedure is incorporated to determine the cluster shape. As a result, our algorithm can recognize a wide range of subspace clusters studied in various literatures, and also the global ball-shaped clusters (living in all dimensions). We carry extensive experiments on both synthetic and real-world datasets, and the results demonstrate the effectiveness of our algorithm.