DBSCAN Clustering Algorithm

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

• • …
• Counter-Example(s):
  • OPTICS Algorithm.
  • Mean-Shift Clustering Algorithm.
• See: Centroid-based Clustering, k-Means Clustering Algorithm.

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References

2015

• (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. 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.

1998

• (Sander et al., 1998) ⇒ Jörg Sander, Martin Ester, Hans-Peter Kriegel, and Xiaowei Xu. (1998). “Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and Its Applications.” In: Data Mining and Knowledge Discovery, 2(2). doi:10.1023/A:1009745219419

1996

• (Ester et al., 1996) ⇒ Martin Ester, Hans-Peter Kriegel, Jörg Sander, and Xiaowei Xu. (1996). “A density-based algorithm for discovering clusters in large spatial databases with noise.” In: Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD-96).

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