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Dirty data commonly exist. Simply discarding a large number of inaccurate points (as noises) could greatly affect clustering results. We argue that dirty data can be repaired and utilized as strong supports in clustering. To this end, we study a novel problem of clustering and repairing over dirty data at the same time. Referring to the minimum change principle in data repairing, the objective is to find a minimum modification of inaccurate points such that the large amount of dirty data candoi:10.1145/2783258.2783317 dblp:conf/kdd/SongLZ15 fatcat:dghyivmv4ndufe7zhmr56jn2ea