The VLDB Journal

, Volume 8, Issue 3, pp 289–304

WaveCluster: a wavelet-based clustering approach for spatial data in very large databases

  • Gholamhosein Sheikholeslami
  • Surojit Chatterjee
  • Aidong Zhang
Regular contribution

DOI: 10.1007/s007780050009

Cite this article as:
Sheikholeslami, G., Chatterjee, S. & Zhang, A. The VLDB Journal (2000) 8: 289. doi:10.1007/s007780050009

Abstract.

Many applications require the management of spatial data in a multidimensional feature space. Clustering large spatial databases is an important problem, which tries to find the densely populated regions in the feature space to be used in data mining, 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 noise (outliers) and the order of input data. We propose WaveCluster, a novel clustering approach based on wavelet transforms, which satisfies all the above requirements. Using the multiresolution property of wavelet transforms, we can effectively identify arbitrarily shaped clusters at different degrees of detail. We also demonstrate that WaveCluster is highly efficient in terms of time complexity. Experimental results on very large datasets are presented, which show the efficiency and effectiveness of the proposed approach compared to the other recent clustering methods.

Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Gholamhosein Sheikholeslami
    • 1
  • Surojit Chatterjee
    • 1
  • Aidong Zhang
    • 1
  1. 1.Department of Computer Science and Engineering, State University of New York at Buffalo, Buffalo, NY 14260, USAUS