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: |
1.0-0 |
Imports: |
Rcpp, graphics, stats, methods |
LinkingTo: |
Rcpp |
Suggests: |
fpc, microbenchmark, testthat, dendextend |
Published: |
2017-02-03 |
Author: |
Michael Hahsler [aut, cre, cph],
Matthew Piekenbrock [aut, 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 and Matthew Piekenbrock. |
NeedsCompilation: |
yes |
Materials: |
README NEWS |
In views: |
Cluster |
CRAN checks: |
dbscan results |