ACM SIGMOD Anthology VLDB dblp.uni-trier.de

WaveCluster: A Multi-Resolution Clustering Approach for Very Large Spatial Databases.

Gholamhosein Sheikholeslami, Surojit Chatterjee, Aidong Zhang: WaveCluster: A Multi-Resolution Clustering Approach for Very Large Spatial Databases. VLDB 1998: 428-439
@inproceedings{DBLP:conf/vldb/SheikholeslamiCZ98,
  author    = {Gholamhosein Sheikholeslami and
               Surojit Chatterjee and
               Aidong Zhang},
  editor    = {Ashish Gupta and
               Oded Shmueli and
               Jennifer Widom},
  title     = {WaveCluster: A Multi-Resolution Clustering Approach for Very
               Large Spatial Databases},
  booktitle = {VLDB'98, Proceedings of 24rd International Conference on Very
               Large Data Bases, August 24-27, 1998, New York City, New York,
               USA},
  publisher = {Morgan Kaufmann},
  year      = {1998},
  isbn      = {1-55860-566-5},
  pages     = {428-439},
  ee        = {db/conf/vldb/SheikholeslamiCZ98.html},
  crossref  = {DBLP:conf/vldb/98},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

Abstract

Many applications require the management of spatial data. Clustering large spatial databases is an important problem which tries to find the densely populated regions in the feature space to be used in datamining, knowledge discovery, or efficient information retrieval. A good clustering approach should be efficient and detect clusters of arbitrary shape. It must be insensitive to the outliers (noise) and the order of input data. We propose WaveCluster, a novel clustering approach based on wavelet transforms, which satisfies all the above requirements. Using multi- resolution property of wavelet transforms, we can effectivelyidentify arbitrary shape clusters at different degrees of accuracy. We also demonstrate that WaveCluster is highly efficient in terms of time complexity. Experimental results on very large data sets are presented which show the efficiency and effectiveness of the proposed approach compared to the other recent clustering methods.

Copyright © 1998 by the VLDB Endowment. Permission to copy without fee all or part of this material is granted provided that the copies are not made or distributed for direct commercial advantage, the VLDB copyright notice and the title of the publication and its date appear, and notice is given that copying is by the permission of the Very Large Data Base Endowment. To copy otherwise, or to republish, requires a fee and/or special permission from the Endowment.


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Printed Edition

Ashish Gupta, Oded Shmueli, Jennifer Widom (Eds.): VLDB'98, Proceedings of 24rd International Conference on Very Large Data Bases, August 24-27, 1998, New York City, New York, USA. Morgan Kaufmann 1998, ISBN 1-55860-566-5
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