DBSCAN
Sign in to saveAlso known as Density-based spatial clustering of applications with noise
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 non-parametric algorithm: 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 the Vinony graph
Vinony's link graph records 207 inbound references to DBSCAN, and connects out to artificial neural network, cluster analysis and k-means clustering.
It is catalogued under the topic Cluster analysis algorithms.
Vinony links it to 19 Wikipedia language editions.
Wikidata facts
Show 2 more facts
- discoverer or inventor
- Hans-Peter Kriegel
- Stack Exchange tag
- stackoverflow.com/tags/dbscan
Sources (3)
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