dbscan (0.9-1)

Density Based Clustering of Applications with Noise (DBSCAN).


A fast reimplementation of the density-based DBSCAN clustering algorithm for spatial data introduced by Ester et al. 'A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise,' 1996. This implementation uses the kd-tree data structure (from library ANN) for faster k-nearest neighbor search. The implementation is many times faster than the R-based implementation in package fpc.

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

License: GPL (>= 2)

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

Released almost 4 years ago.