dbscan (0.9-8)

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.

Maintainer: Michael Hahsler
Author(s): Michael Hahsler [aut, cre, cph], Matthew Piekenbrock [ctb, cph], Sunil Arya [ctb, cph], David Mount [ctb, cph]

License: GPL (>= 2)

Uses: Rcpp, fpc, testthat, microbenchmark
Reverse depends: funtimes, ktaucenters, ParBayesianOptimization
Reverse suggests: gsrc, largeVis, opticskxi, OTclust, performance, shipunov, smotefamily, stranger, supc

Released over 3 years ago.