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

person("Matthew", "Piekenbrock", role = c("ctb", "cph")),
Description: 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.

person("Matthew", "Piekenbrock", role = c("aut", "cph")),
Suggests: fpc, microbenchmark, testthat, dendextend

the clustering structure) clustering algorithms HDBSCAN (hierarchical DBSCAN) and the LOF (local outlier
Suggests: fpc, microbenchmark, testthat, dendextend, igraph, knitr

Title: Density-Based Spatial Clustering of Applications with Noise
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>.
Imports: Rcpp (>= 1.0.0), graphics, stats
Suggests: fpc, microbenchmark, testthat, dendextend, igraph, knitr,