Porównaj metody

	DBSCAN	HDBSCAN	Klasteryzacja hierarchiczna
Dziedzina	Uczenie maszynowe	Uczenie maszynowe	Uczenie maszynowe
Rodzina	Machine learning	Machine learning	Machine learning
Rok powstania	1996	2013	1963
Twórca	Ester, M., Kriegel, H.-P., Sander, J. & Xu, X.	Campello, R. J. G. B.; Moulavi, D.; Sander, J.	Ward, J. H.
Typ	Density-based clustering algorithm	Hierarchical density-based clustering	Unsupervised clustering (agglomerative)
Źródło pierwotne	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.	Campello, R. J. G. B., Moulavi, D., & Sander, J. (2013). Density-Based Clustering Based on Hierarchical Density Estimates. In J. Pei et al. (Eds.), Advances in Knowledge Discovery and Data Mining. PAKDD 2013. Lecture Notes in Computer Science, vol. 7819 (pp. 160–172). Springer, Berlin, Heidelberg.	Ward, J. H. (1963). Hierarchical Grouping to Optimize an Objective Function. Journal of the American Statistical Association, 58(301), 236–244.
Inne nazwy	DBSCAN Kümeleme, density-based clustering, density-based spatial clustering	HDBSCAN, Hierarchical DBSCAN, hierarchical density-based clustering, HDBSCAN*	Hiyerarşik Kümeleme, hiyerarşik kümeleme, agglomerative clustering, hierarchical agglomerative clustering
Pokrewne	3	3	4
Podsumowanie	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.	HDBSCAN (Hierarchical Density-Based Spatial Clustering of Applications with Noise) is a density-based clustering algorithm introduced by Campello, Moulavi, and Sander in 2013. It extends DBSCAN by building a full hierarchy of density-based clusters across all density scales and then extracting a stable flat partition, making it robust to datasets where cluster densities vary substantially across regions.	Hierarchical clustering is an unsupervised method that groups observations into nested clusters and draws the result as a dendrogram, so the number of clusters need not be fixed in advance. Its agglomerative form rests on the objective-function grouping criterion introduced by Joe Ward in 1963.