Módszerek összehasonlítása

	Robust HDBSCAN	DBSCAN
Tudományterület	Gépi tanulás	Gépi tanulás
Módszercsalád	Machine learning	Machine learning
Keletkezés éve	2015	1996
Megalkotó	Campello, R.J.G.B.; Moulavi, D.; Zimek, A.; Sander, J.	Ester, M., Kriegel, H.-P., Sander, J. & Xu, X.
Típus	Hierarchical density-based clustering with robust single-linkage	Density-based clustering algorithm
Alapmű	Campello, R.J.G.B., Moulavi, D., Zimek, A. & Sander, J. (2015). Hierarchical Density Estimates for Data Clustering, Visualization, and Outlier Detection. ACM Transactions on Knowledge Discovery from Data, 10(1), 5.	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.
Alternatív nevek	HDBSCAN*, Robust HDBSCAN*, robust hierarchical density clustering, robust single-linkage HDBSCAN	DBSCAN Kümeleme, density-based clustering, density-based spatial clustering
Kapcsolódó	4	3
Összefoglaló	Robust HDBSCAN (HDBSCAN*) extends the original HDBSCAN algorithm with a robust single-linkage framework that handles noise, outliers, and clusters of varying densities more reliably. Introduced by Campello et al. (2015), it converts any density-based hierarchy into a stable flat clustering while explicitly modeling noise points — without requiring the user to pre-specify the number of clusters.	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.