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 HDBSCAN (hierarchical DBSCAN) 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.

Maintainer: Michael Hahsler <mhahsler at lyle.smu.edu>

Author(s): Michael Hahsler*, Matthew Piekenbrock*, Sunil Arya*, David Mount*

Install package and any missing dependencies by running this line in your R console:

install.packages("dbscan")

Depends
Imports Rcpp, graphics, stats, methods
Suggests fpc, microbenchmark, testthat, dendextend, igraph, knitr
Enhances
Linking to Rcpp
Reverse
depends
funtimes
Reverse
imports
AFM, cordillera, gsrc, haploReconstruct, stream
Reverse
suggests
largeVis, smotefamily
Reverse
enhances
Reverse
linking to

Package dbscan
Materials
URL
Task Views Cluster
Version 1.1-1
Published 2017-03-19
License GPL (>= 2)
BugReports https://github.com/mhahsler/dbscan
SystemRequirements C++11
NeedsCompilation yes
Citation
CRAN checks dbscan check results
Package source dbscan_1.1-1.tar.gz