Density Based Clustering of Applications with Noise (DBSCAN)


Density Based Clustering of Applications with Noise (DBSCAN) and Related Algorithms provides a fast reimplementation of several density- based algorithms of the DBSCAN family for spatial data. Includes the clustering algorithms DBSCAN (density-based spatial clustering of applications with noise) and HDBSCAN (hierarchical DBSCAN), the ordering algorithm OPTICS (ordering points to identify the clustering structure), and the outlier detection algorithm LOF (local outlier factor). The implementations use the kd-tree data structure (from library ANN) for faster k-nearest neighbor search. An R interface to fast kNN and fixed-radius NN search is also provided. Hahsler, Piekenbrock and Doran (2019) <doi:10.18637/jss.v091.i01>.

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