Porovnat metody

	DBSCAN	Semi-supervidované DBSCAN
Obor	Strojové učení	Strojové učení
Rodina	Machine learning	Machine learning
Rok vzniku	1996	2000s
Tvůrce	Ester, M., Kriegel, H.-P., Sander, J. & Xu, X.	Ester, M. et al. (DBSCAN base); semi-supervised extensions by multiple authors (2000s–2010s)
Typ	Density-based clustering algorithm	Constrained density-based clustering
Původní zdroj	Ester, M., Kriegel, H.-P., Sander, J. & Xu, X. (1996). A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. Proceedings of the 2nd KDD, 226–231.	Ester, M., Kriegel, H.-P., Sander, J., & Xu, X. (1996). A density-based algorithm for discovering clusters in large spatial databases with noise. In Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining (KDD-96), pp. 226–231. AAAI Press.
Další názvy	DBSCAN Kümeleme, density-based clustering, density-based spatial clustering	Constrained DBSCAN, SS-DBSCAN, DBSCAN with must-link/cannot-link constraints, seeded DBSCAN
Příbuzné	3	5
Shrnutí	DBSCAN is a density-based clustering algorithm, introduced by Ester, Kriegel, Sander and Xu in 1996, that groups together points lying in dense regions and flags points in sparse regions as noise. It is effective on noisy data and on clusters of irregular, non-spherical shapes.	Semi-supervised DBSCAN extends the canonical density-based clustering algorithm (Ester et al., 1996) by incorporating a small set of pairwise or label constraints — must-link pairs that must share a cluster, cannot-link pairs that must be separated, or a handful of known labels — to guide cluster formation while retaining DBSCAN's ability to discover arbitrary-shaped clusters and flag noise points.