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DBSCAN
Density-based data clustering algorithm
DBSCAN
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 that does not assume a fixed parametric model for the clusters, such as Gaussian blobs, and it does not require the number of clusters to be specified in advance. Given a set of points in some space, it groups together points that are closely packed (points with many nearby neighbors), and marks as outliers points that lie alone in low-density regions (those whose nearest neighbors are too far away). DBSCAN is one of the most commonly used and cited clustering algorithms. In 2014, the algorithm was awarded the Test of Time Award (an award given to algorithms which have received substantial attention in theory and practice) at the leading data mining conference, ACM SIGKDD. As of July 2020, the follow-up paper "DBSCAN Revisited, Revisited: Why and How You Should (Still...
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