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DOI: 10.4230/LIPIcs.ISAAC.2017.25
Faster DBScan and HDBScan in Low-Dimensional Euclidean Spaces

Authors: Mark de Berg, Ade Gunawan, and Marcel Roeloffzen

Published in: LIPIcs, Volume 92, 28th International Symposium on Algorithms and Computation (ISAAC 2017)

Abstract
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.
Cite as

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)

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@InProceedings{deberg_et_al:LIPIcs.ISAAC.2017.25,
  author =	{de Berg, Mark and Gunawan, Ade and Roeloffzen, Marcel},
  title =	{{Faster DBScan and HDBScan in Low-Dimensional Euclidean Spaces}},
  booktitle =	{28th International Symposium on Algorithms and Computation (ISAAC 2017)},
  pages =	{25:1--25:13},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-054-5},
  ISSN =	{1868-8969},
  year =	{2017},
  volume =	{92},
  editor =	{Okamoto, Yoshio and Tokuyama, Takeshi},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.ISAAC.2017.25},
  URN =		{urn:nbn:de:0030-drops-82102},
  doi =		{10.4230/LIPIcs.ISAAC.2017.25},
  annote =	{Keywords: Density-based clustering, hierarchical clustering}
}
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