A fast reimplementation of several density-based algorithms of
the DBSCAN family for spatial data. Includes the clustering algorithms
DBSCAN (density-based spatial clustering of applications with noise)
and HDBSCAN (hierarchical DBSCAN), the ordering algorithm
OPTICS (ordering points to identify the clustering structure),
and the outlier detection algorithm LOF (local outlier factor).
The implementations use 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.
Hahsler, Piekenbrock and Doran (2019) <doi:10.18637/jss.v091.i01>.
| Version: |
1.1-8 |
| Imports: |
Rcpp (≥ 1.0.0), graphics, stats |
| LinkingTo: |
Rcpp |
| Suggests: |
fpc, microbenchmark, testthat, dendextend, igraph, knitr, rmarkdown |
| Published: |
2021-04-27 |
| 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. |
| URL: |
https://github.com/mhahsler/dbscan |
| NeedsCompilation: |
yes |
| SystemRequirements: |
C++11 |
| Citation: |
dbscan citation info |
| Materials: |
README NEWS |
| In views: |
Cluster |
| CRAN checks: |
dbscan results |