DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is a data clustering algorithm proposed by Martin Ester, Hans-Peter Kriegel, Jö rg Sander and Xiaowei Xu in 1996.[1] It is a density-based clustering algorithm because it finds a number of clusters starting from the estimated density distribution of corresponding nodes. DBSCAN is one of the most common clustering algorithms and also most cited in scientific literature.[2] OPTICS can be seen as a generalization of DBSCAN to multiple ranges, effectively replacing the parameter with a maximum search radius.

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DBSCAN - Wikipedia	the free encyclopedia.pdf

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