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 |