dbscan: Fast Density-based Clustering with R

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. Compared to other implementations, dbscan offers open-source implementations using C++ and advanced data structures like k-d trees to speed up computation. An important advantage of this implementation is that it is up-to-date with several primary advancements that have been added since their original publications, including artifact corrections and dendrogram extraction methods for OPTICS. Experiments with dbscan’s implementation of DBSCAN and OPTICS compared and other libraries such as FPC, ELKI, WEKA, PyClustering, SciKit-Learn and SPMF suggest that dbscan provides a very efficient implementation.