dbscan-package: dbscan: Density-Based Spatial Clustering of Applications with...

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

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

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) \Sexpr[results=rd]{tools:::Rd_expr_doi("10.18637/jss.v091.i01")}.

Key functions

• Clustering: dbscan(), hdbscan(), optics(), jpclust(), sNNclust()

• Outliers: lof(), glosh(), pointdensity()

• Nearest Neighbors: kNN(), frNN(), sNN()

Author(s)

Maintainer: Michael Hahsler [e-mail] [copyright holder]

Authors:

• Matthew Piekenbrock [copyright holder]

Other contributors:

• Sunil Arya [contributor, copyright holder]

• David Mount [contributor, copyright holder]

References

Hahsler M, Piekenbrock M, Doran D (2019). dbscan: Fast Density-Based Clustering with R. Journal of Statistical Software, 91(1), 1-30. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.18637/jss.v091.i01")}

See Also

Useful links:

• https://github.com/mhahsler/dbscan

• Report bugs at https://github.com/mhahsler/dbscan/issues

dbscan documentation built on June 29, 2024, 1:07 a.m.