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Mining Projected Clusters in High-Dimensional Spaces
2009
IEEE Transactions on Knowledge and Data Engineering
Clustering high-dimensional data has been a major challenge due to the inherent sparsity of the points. Most existing clustering algorithms become substantially inefficient if the required similarity measure is computed between data points in the full-dimensional space. To address this problem, a number of projected clustering algorithms have been proposed. However, most of them encounter difficulties when clusters hide in subspaces with very low dimensionality. These challenges motivate our
doi:10.1109/tkde.2008.162
fatcat:6osss5zq4fbsrfms6jcn64adqi