A fast reimplementation of several density-based algorithms
of the DBSCAN family. 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), shared nearest neighbor
clustering, and the outlier detection algorithms LOF (local outlier
factor) and GLOSH (global-local outlier score from hierarchies). 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: |
1.2.2 |
| Depends: |
R (≥ 3.2.0) |
| Imports: |
generics, graphics, Rcpp (≥ 1.0.0), stats |
| LinkingTo: |
Rcpp |
| Suggests: |
dendextend, fpc, igraph, knitr, microbenchmark, rmarkdown, testthat (≥ 3.0.0), tibble |
| Published: |
2025-01-26 |
| Author: |
Michael Hahsler 
[aut, cre, cph] (<https://orcid.org/0000-0003-2716-1405>),
Matthew Piekenbrock [aut, cph],
Sunil Arya [ctb, cph],
David Mount [ctb, cph],
Claudia Malzer [ctb] |
| 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 |
| Citation: |
dbscan citation info |
| Materials: |
README NEWS |
| In views: |
Cluster |
| CRAN checks: |
dbscan results |