Сравнение методов

	DBSCAN	OPTICS
Область	Машинное обучение	Машинное обучение
Семейство	Machine learning	Machine learning
Год появления	1996	1999
Автор метода	Ester, M., Kriegel, H.-P., Sander, J. & Xu, X.	Ankerst, M.; Breunig, M. M.; Kriegel, H.-P.; Sander, J.
Тип	Density-based clustering algorithm	Density-based clustering (reachability ordering)
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.	Ankerst, M., Breunig, M. M., Kriegel, H.-P., & Sander, J. (1999). OPTICS: Ordering points to identify the clustering structure. ACM SIGMOD Record, 28(2), 49–60.
Другие названия	DBSCAN Kümeleme, density-based clustering, density-based spatial clustering	OPTICS, Ordering Points To Identify the Clustering Structure, density-based clustering with reachability plot, generalized DBSCAN
Связанные	3	3
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.	OPTICS (Ordering Points To Identify the Clustering Structure) is a density-based clustering algorithm introduced by Ankerst, Breunig, Kriegel, and Sander in 1999. It generalizes DBSCAN by processing points in an ordering that encodes the full density-based cluster structure of a dataset, enabling the detection of clusters of varying densities through a reachability plot rather than requiring a fixed global density threshold.