Сравнение методов

	DBSCAN	Полуавтоматический DBSCAN
Область	Машинное обучение	Машинное обучение
Семейство	Machine learning	Machine learning
Год появления	1996	2000s
Автор метода	Ester, M., Kriegel, H.-P., Sander, J. & Xu, X.	Ester, M. et al. (DBSCAN base); semi-supervised extensions by multiple authors (2000s–2010s)
Тип	Density-based clustering algorithm	Constrained density-based clustering
Основополагающий источник	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.
Другие названия	DBSCAN Kümeleme, density-based clustering, density-based spatial clustering	Constrained DBSCAN, SS-DBSCAN, DBSCAN with must-link/cannot-link constraints, seeded DBSCAN
Связанные	3	5
Сводка	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.