Package: dbscan
Version: 1.1-12
Date: 2023-11-28
Title: Density-Based Spatial Clustering of Applications with Noise
        (DBSCAN) and Related Algorithms
Authors@R: 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")))
Description: 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>.
Imports: Rcpp (>= 1.0.0), graphics, stats
LinkingTo: Rcpp
Suggests: fpc, microbenchmark, testthat, dendextend, igraph, knitr,
        rmarkdown
VignetteBuilder: knitr
URL: https://github.com/mhahsler/dbscan
BugReports: https://github.com/mhahsler/dbscan/issues
License: GPL (>= 2)
Copyright: ANN library is copyright by University of Maryland, Sunil
        Arya and David Mount. All other code is copyright by Michael
        Hahsler and Matthew Piekenbrock.
Encoding: UTF-8
RoxygenNote: 7.2.3
NeedsCompilation: yes
Packaged: 2023-11-28 14:45:10 UTC; hahsler
Author: Michael Hahsler [aut, cre, cph],
  Matthew Piekenbrock [aut, cph],
  Sunil Arya [ctb, cph],
  David Mount [ctb, cph]
Maintainer: Michael Hahsler <mhahsler@lyle.smu.edu>
Repository: CRAN
Date/Publication: 2023-11-28 17:10:05 UTC
Built: R 4.3.2; x86_64-pc-linux-gnu; 'Sat, 23 Dec 2023 17:31:10 +0100'; unix
