dbscan: Fast Density-Based Clustering with R

Michael Hahsler

doi:10.18637/jss.v091.i01

dbscan: Fast Density-Based Clustering with R

2019-01-01

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AI

DOI：10.18637/jss.v091.i01

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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.

Keyword:

DBSCAN

OPTICS

density-based clustering

hierarchical clustering

dbscan: Fast Density-Based Clustering with R

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