DBRS: a density-based spatial clustering method with random sampling

  • Wang X
  • Hamilton H
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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.

Author-supplied keywords

  • density
  • varying densities
  • varying shape

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  • Xin Wang

  • Howard J Hamilton

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