dbscan v0.9-2

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by Michael Hahsler

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 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.

Functions in dbscan

Name Description
kNN Find the k Nearest Neighbors
kNNdist Calculate and plot the k-Nearest Neighbor Distance
lof Local Outlier Factor Score
frNN Find the Fixed Radius Nearest Neighbors
optics OPTICS
dbscan DBSCAN
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Details

Date 2015-08-11
Copyright ANN library is copyright by University of Maryland, Sunil Arya and David Mount. All other code is copyright by Michael Hahsler.
License GPL (>= 2)
LinkingTo Rcpp
NeedsCompilation yes
Packaged 2015-08-11 19:09:15 UTC; hahsler
Repository CRAN
Date/Publication 2015-08-12 00:35:38

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