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To gain insight into today's large data resources, data mining provides automatic aggregation techniques. Clustering aims at grouping data such that objects within groups are similar while objects in different groups are dissimilar. In scenarios with many attributes or with noise, clusters are often hidden in subspaces of the data and do not show up in the full dimensional space. For these applications, subspace clustering methods aim at detecting clusters in any subspace. Existing subspacedoi:10.1109/icdm.2007.49 dblp:conf/icdm/AssentKMS07 fatcat:xpcpscejrjbpzazkrgannqyl6q