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dbscan : Fast Density-Based Clustering with R

2019; Foundation for Open Access Statistics; Volume: 91; Issue: 1 Linguagem: Inglês

10.18637/jss.v091.i01

1548-7660

Michael Hahsler, Matthew Piekenbrock, Derek Doran,

Data Mining Algorithms and Applications

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.