c(person("Michael", "Hahsler", role = c("aut", "cre", "cph"),
email = "mhahsler@lyle.smu.edu"),
person("Matthew", "Piekenbrock", role = c("aut", "cph")),
person("Sunil", "Arya", role = c("ctb", "cph")),
person("David", "Mount", role = c("ctb", "cph")))
ANN library is copyright by University of Maryland, Sunil
Arya and David Mount. All other code is copyright by Michael
Hahsler and Matthew Piekenbrock.
Michael Hahsler [aut, cre, cph],
Matthew Piekenbrock [aut, cph],
Sunil Arya [ctb, cph],
David Mount [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 HDBSCAN (hierarchical DBSCAN) 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.
fpc, microbenchmark, testthat, dendextend, igraph, knitr

Density-Based Spatial Clustering of Applications with Noise
(DBSCAN) and Related Algorithms
A fast reimplementation of several density-based algorithms of
the DBSCAN family. Includes the clustering algorithms DBSCAN (density-based
spatial clustering of applications with noise) and HDBSCAN (hierarchical
DBSCAN), the ordering algorithm OPTICS (ordering points to identify the
clustering structure), shared nearest neighbor clustering, and the outlier
detection algorithms LOF (local outlier factor) and GLOSH (global-local
outlier score from hierarchies). 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.
Hahsler, Piekenbrock and Doran (2019) <doi:10.18637/jss.v091.i01>.
fpc, microbenchmark, testthat, dendextend, igraph, knitr,
rmarkdown

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.
Rcpp (>= 0.12.12), graphics, stats, methods
fpc, microbenchmark, testthat, dendextend, igraph, knitr,
DMwR