DBSCAN

Correlation clustering

Algorithm

Data mining

Artificial intelligence

Computer science

Computation

Cluster analysis

Search engine indexing

CURE data clustering algorithm

Journal of Statistical Software

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.

<b>dbscan</b>: Fast Density-Based Clustering with <i>R</i>

dbscan: Fast Density-Based Clustering with R