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| Authors: | Michael Hahsler, Matthew Piekenbrock, Derek Doran | ||||||
| Title: | dbscan: Fast Density-Based Clustering with R | ||||||
| Abstract: | This article describes the implementation and use of the R package dbscan, which provides complete and fast implementations of the popular density-based clustering algorithm DBSCAN and the augmented ordering algorithm OPTICS. Package dbscan uses advanced open-source spatial indexing data structures implemented in C++ to speed up computation. An important advantage of this implementation is that it is up-to-date with several improvements that have been added since the original algorithms were publications (e.g., artifact corrections and dendrogram extraction methods for OPTICS). We provide a consistent presentation of the DBSCAN and OPTICS algorithms, and compare dbscan's implementation with other popular libraries such as the R package fpc, ELKI, WEKA, PyClustering, SciKit-Learn, and SPMF in terms of available features and using an experimental comparison. | ||||||
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Page views:: 744. Submitted: 2017-02-06. Published: 2019-10-31. |
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| Paper: |
dbscan: Fast Density-Based Clustering with R
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| DOI: |
10.18637/jss.v091.i01
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This work is licensed under the licenses Paper: Creative Commons Attribution 3.0 Unported License Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license. |