License: 
Creative Commons Attribution 3.0 Unported license (CC BY 3.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.ISAAC.2017.25
URN: urn:nbn:de:0030-drops-82102
URL: https://drops.dagstuhl.de/opus/volltexte/2017/8210/
de Berg, Mark ;
Gunawan, Ade ;
Roeloffzen, Marcel
Faster DBScan and HDBScan in Low-Dimensional Euclidean Spaces
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.
BibTeX - Entry
@InProceedings{deberg_et_al:LIPIcs:2017:8210,
author = {Mark de Berg and Ade Gunawan and Marcel Roeloffzen},
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 = {Yoshio Okamoto and Takeshi Tokuyama},
publisher = {Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik},
address = {Dagstuhl, Germany},
URL = {http://drops.dagstuhl.de/opus/volltexte/2017/8210},
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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Keywords: |
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Density-based clustering, hierarchical clustering |
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Collection: |
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28th International Symposium on Algorithms and Computation (ISAAC 2017) |
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Issue Date: |
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2017 |
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Date of publication: |
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07.12.2017 |
