dbscan: Density Based Clustering of Applications with Noise (DBSCAN) and
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.
||Rcpp, graphics, stats, methods
||fpc, microbenchmark, testthat, dendextend
||Michael Hahsler [aut, cre, cph],
Matthew Piekenbrock [aut, cph],
Sunil Arya [ctb, cph],
David Mount [ctb, cph]
||Michael Hahsler <mhahsler at lyle.smu.edu>
||GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
||ANN library is copyright by University of Maryland, Sunil
Arya and David Mount. All other code is copyright by Michael
Hahsler and Matthew Piekenbrock.
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