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: |
1.2-0 |
| Depends: |
R (≥ 3.2.0) |
| Imports: |
Rcpp (≥ 1.0.0), graphics, stats, generics |
| LinkingTo: |
Rcpp |
| Suggests: |
fpc, microbenchmark, testthat (≥ 3.0.0), dendextend, igraph, knitr, rmarkdown, tibble, rlang |
| Published: |
2024-06-28 |
| DOI: |
10.32614/CRAN.package.dbscan |
| 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 |
| Citation: |
dbscan citation info |
| Materials: |
README NEWS |
| In views: |
Cluster |
| CRAN checks: |
dbscan results |