dbscan (1.1-5)

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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 HDBSCAN (hierarchical DBSCAN) and the LOF (local outlier factor) algorithm. 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. See Hahsler M, Piekenbrock M and Doran D (2019) .

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

License: GPL (>= 2)

Uses: Rcpp, fpc, igraph, testthat, DMwR, microbenchmark, knitr, dendextend
Reverse depends: funtimes, ktaucenters, ParBayesianOptimization
Reverse suggests: FCPS, gsrc, ksharp, largeVis, opticskxi, OTclust, performance, shipunov, smotefamily, stranger, supc

Released 5 months ago.

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