dbscan v0.9-3
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Density Based Clustering of Applications with Noise (DBSCAN) and Related Algorithms
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.
Functions in dbscan
| Name | Description | |
| kNNdist | Calculate and plot the k-Nearest Neighbor Distance | |
| frNN | Find the Fixed Radius Nearest Neighbors | |
| optics | OPTICS | |
| lof | Local Outlier Factor Score | |
| kNN | Find the k Nearest Neighbors | |
| dbscan | DBSCAN | |
| No Results! | ||
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Details
| Date | 2015-09-2 |
| Copyright | ANN library is copyright by University of Maryland, Sunil Arya and David Mount. All other code is copyright by Michael Hahsler. |
| License | GPL (>= 2) |
| LinkingTo | Rcpp |
| NeedsCompilation | yes |
| Packaged | 2015-09-03 12:44:19 UTC; hahsler |
| Repository | CRAN |
| Date/Publication | 2015-09-03 16:53:20 |
| suggests | fpc , microbenchmark |
| imports | Rcpp |
| Contributors | Michael Hahsler, David Mount, Sunil Arya |
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[](http://www.rdocumentation.org/packages/dbscan)