dbscan
Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Related Algorithms
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.
- Version1.2.2
- R versionR (≥ 3.2.0)
- LicenseGPL-2
- LicenseGPL-3
- Needs compilation?Yes
- dbscan citation info
- Last release01/26/2025
Documentation
Team
Michael Hahsler
MaintainerShow author detailsDavid Mount
Show author detailsRolesContributor, Copyright holderMatthew Piekenbrock
Show author detailsRolesAuthor, Copyright holderSunil Arya
Show author detailsRolesContributor, Copyright holderClaudia Malzer
Show author detailsRolesContributor
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- Imports2 packages
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