Faster DBScan and HDBScan in Low-Dimensional Euclidean Spaces

Authors Mark de Berg, Ade Gunawan, Marcel Roeloffzen

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Mark de Berg
Ade Gunawan
Marcel Roeloffzen

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Mark de Berg, Ade Gunawan, and Marcel Roeloffzen. Faster DBScan and HDBScan in Low-Dimensional Euclidean Spaces. In 28th International Symposium on Algorithms and Computation (ISAAC 2017). Leibniz International Proceedings in Informatics (LIPIcs), Volume 92, pp. 25:1-25:13, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2017)


We present a new algorithm for the widely used density-based clustering method DBScan. Our algorithm computes the DBScan-clustering in O(n log n) time in R^2, irrespective of the scale parameter \eps, but assuming the second parameter MinPts is set to a fixed constant, as is the case in practice. We also present an O(n log n) randomized algorithm for HDBScan in the plane---HDBScans is a hierarchical version of DBScan introduced recently---and we show how to compute an approximate version of HDBScan in near-linear time in any fixed dimension.
  • Density-based clustering
  • hierarchical clustering


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