dbscan: Density Based Clustering of Applications with Noise (DBSCAN) and Related Algorithms

A fast reimplementation of several density-based algorithms of the DBSCAN family for spatial data. 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), and the outlier detection algorithm LOF (local outlier factor). 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>.

Package details

AuthorMichael Hahsler [aut, cre, cph], Matthew Piekenbrock [aut, cph], Sunil Arya [ctb, cph], David Mount [ctb, cph]
MaintainerMichael Hahsler <mhahsler@lyle.smu.edu>
LicenseGPL (>= 2)
Version1.1-6
URL https://github.com/mhahsler/dbscan
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:
install.packages("dbscan")

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dbscan documentation built on Feb. 26, 2021, 9:06 a.m.