dbscan: Density Based Clustering of Applications with Noise (DBSCAN) and Related Algorithms

A fast reimplementation of several density-based algorithms of the DBSCAN family for spatial data. Includes the DBSCAN (density-based spatial clustering of applications with noise) and OPTICS (ordering points to identify the clustering structure) clustering algorithms and the LOF (local outlier factor) algorithm. The implementations uses the kd-tree data structure (from library ANN) for faster k-nearest neighbor search. An R interface to fast kNN and fixed-radius NN search is also provided.

Version: 0.9-6
Imports: Rcpp, graphics, stats, methods
LinkingTo: Rcpp
Suggests: fpc, microbenchmark, testthat
Published: 2015-12-15
Author: Michael Hahsler [aut, cre, cph], Sunil Arya [ctb, cph], David Mount [ctb, cph]
Maintainer: Michael Hahsler <mhahsler at lyle.smu.edu>
BugReports: https://github.com/mhahsler/dbscan/issues
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
Copyright: ANN library is copyright by University of Maryland, Sunil Arya and David Mount. All other code is copyright by Michael Hahsler.
NeedsCompilation: yes
Materials: README NEWS
In views: Cluster
CRAN checks: dbscan results

Downloads:

Reference manual: dbscan.pdf
Package source: dbscan_0.9-6.tar.gz
Windows binaries: r-devel: dbscan_0.9-6.zip, r-release: dbscan_0.9-6.zip, r-oldrel: dbscan_0.9-6.zip
OS X Snow Leopard binaries: r-release: dbscan_0.9-6.tgz, r-oldrel: not available
OS X Mavericks binaries: r-release: dbscan_0.9-6.tgz
Old sources: dbscan archive

Reverse dependencies:

Reverse imports: stream