DBSCAN Clustering Algorithm

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A DBSCAN Clustering Algorithm is a density-based clustering algorithm that ...



  • (Wikipedia, 2015) ⇒ http://en.wikipedia.org/wiki/DBSCAN Retrieved:2015-1-16.
    • Density-based spatial clustering of applications with noise (DBSCAN) is a data clustering algorithm proposed by Martin Ester, Hans-Peter Kriegel, Jörg Sander and Xiaowei Xu in 1996. 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. [1] OPTICS can be seen as a generalization of DBSCAN to multiple ranges, effectively replacing the ε parameter with a maximum search radius. In 2014, the algorithm was awarded the test of time award (an award given to algorithms which have received substantial attention in theory and practise) at the leading data mining conference, KDD.
  1. [1] Most cited data mining articles according to Microsoft academic search; DBSCAN is on rank 24, when accessed on: 4/18/2010