Density Based Clustering of Applications with Noise (DBSCAN)
c(person("Michael", "Hahsler", role = c("aut", "cre", "cph"),
email = "mhahsler@lyle.smu.edu"),
person("Sunil", "Arya", role = c("ctb", "cph")),
person("David", "Mount", role = c("ctb", "cph")))
ANN library is copyright University of Maryland and Sunil
Arya and David Mount.
A fast reimplementation of the DBSCAN clustering algorithm using the kd-tree data structure for speedup.
Michael Hahsler [aut, cre, cph],
Sunil Arya [ctb, cph],
David Mount [ctb, cph]

ANN library is copyright University of Maryland, Sunil Arya
and David Mount.
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.

Density Based Clustering of Applications with Noise (DBSCAN) and
Related Algorithms
ANN library is copyright by University of Maryland, Sunil
Arya and David Mount. All other code is copyright by Michael
Hahsler.
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.

c(person("Michael", "Hahsler", role = c("aut", "cre", "cph"),
email = "mhahsler@lyle.smu.edu"),
person("Matthew", "Piekenbrock", role = c("ctb", "cph")),
person("Sunil", "Arya", role = c("ctb", "cph")),
person("David", "Mount", role = c("ctb", "cph")))
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.
Michael Hahsler [aut, cre, cph],
Matthew Piekenbrock [ctb, cph],
Sunil Arya [ctb, cph],
David Mount [ctb, cph]