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Erschienen in:
2003 | OriginalPaper | Buchkapitel
DBRS: A Density-Based Spatial Clustering Method with Random Sampling
verfasst von : Xin Wang, Howard J. Hamilton
Erschienen in: Advances in Knowledge Discovery and Data Mining
Verlag: Springer Berlin Heidelberg
Enthalten in: Professional Book Archive
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In this paper, we propose a novel density-based spatial clustering method called DBRS. The algorithm can identify clusters of widely varying shapes, clusters of varying densities, clusters which depend on non-spatial attributes, and approximate clusters in very large databases. DBRS achieves these results by repeatedly picking an unclassified point at random and examining its neighborhood. A theoretical comparison of DBRS and DBSCAN, a well-known density-based algorithm, is also given in the paper.