Published by the Foundation for Open Access Statistics Editors-in-chief: Bettina Grün, Torsten Hothorn, Rebecca Killick, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
Authors: Michael Hahsler, Matthew Piekenbrock, Derek Doran
Title: dbscan: Fast Density-Based Clustering with R
Abstract: This article describes the implementation and use of the R package dbscan, which provides complete and fast implementations of the popular density-based clustering algorithm DBSCAN and the augmented ordering algorithm OPTICS. Package dbscan uses advanced open-source spatial indexing data structures implemented in C++ to speed up computation. An important advantage of this implementation is that it is up-to-date with several improvements that have been added since the original algorithms were publications (e.g., artifact corrections and dendrogram extraction methods for OPTICS). We provide a consistent presentation of the DBSCAN and OPTICS algorithms, and compare dbscan's implementation with other popular libraries such as the R package fpc, ELKI, WEKA, PyClustering, SciKit-Learn, and SPMF in terms of available features and using an experimental comparison.

Page views:: 6513. Submitted: 2017-02-06. Published: 2019-10-31.
Paper: dbscan: Fast Density-Based Clustering with R     Download PDF (Downloads: 4569)
Supplements:
dbscan_1.1-5.tar.gz: R source package Download (Downloads: 137; 1MB)
v91i01.R: R replication code Download (Downloads: 166; 1KB)
v91i01-benchmark.zip: Benchmark replication materials Download (Downloads: 79; 33MB)
DOI: 10.18637/jss.v091.i01

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Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.