Efficient Algorithms for Mining Outliers from Large Data Sets.

@inproceedings{DBLP:conf/sigmod/RamaswamyRS00,
  author    = {Sridhar Ramaswamy and
               Rajeev Rastogi and
               Kyuseok Shim},
  editor    = {Weidong Chen and
               Jeffrey F. Naughton and
               Philip A. Bernstein},
  title     = {Efficient Algorithms for Mining Outliers from Large Data Sets},
  booktitle = {Proceedings of the 2000 ACM SIGMOD International Conference on
               Management of Data, May 16-18, 2000, Dallas, Texas, USA},
  publisher = {ACM},
  year      = {2000},
  isbn      = {1-58113-218-2},
  pages     = {427-438},
  ee        = {http://doi.acm.org/10.1145/342009.335437, db/conf/sigmod/RamaswamyRS00.html},
  crossref  = {DBLP:conf/sigmod/2000},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

Abstract

Copyright © 2000 by the ACM, Inc., used by permission. Permission to make digital or hard copies is granted provided that copies are not made or distributed for profit or direct commercial advantage, and that copies show this notice on the first page or initial screen of a display along with the full citation.

Online Edition

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Referenced by

1. Markus M. Breunig, Hans-Peter Kriegel, Raymond T. Ng, Jörg Sander: LOF: Identifying Density-Based Local Outliers. SIGMOD Conference 2000: 93-104