WaveCluster: A Multi-Resolution Clustering Approach for Very Large Spatial Databases
Gholamhosein Sheikholeslami, Surojit Chatterjee, Aidong Zhang
Full Paper (PDF)

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.

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BIBTEX

@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},
crossref = {DBLP:conf/vldb/98},
bibsource = {DBLP, http://dblp.uni-trier.de}
}


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