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.
CITATION STYLE
Wang, X., & Hamilton, H. J. (2003). DBRS: A density-based spatial clustering method with random sampling. In Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science) (Vol. 2637, pp. 563–575). Springer Verlag. https://doi.org/10.1007/3-540-36175-8_56
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