Σύγκριση μεθόδων

	ΟΠΤΙΚΗ	HDBSCAN	Ιεραρχική ομαδοποίηση
Πεδίο	Μηχανική Μάθηση	Μηχανική Μάθηση	Μηχανική Μάθηση
Οικογένεια	Machine learning	Machine learning	Machine learning
Έτος προέλευσης	1999	2013	1963
Δημιουργός	Ankerst, M.; Breunig, M. M.; Kriegel, H.-P.; Sander, J.	Campello, R. J. G. B.; Moulavi, D.; Sander, J.	Ward, J. H.
Τύπος	Density-based clustering (reachability ordering)	Hierarchical density-based clustering	Unsupervised clustering (agglomerative)
Θεμελιώδης πηγή	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.	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.
Εναλλακτικές ονομασίες	OPTICS, Ordering Points To Identify the Clustering Structure, density-based clustering with reachability plot, generalized DBSCAN	HDBSCAN, Hierarchical DBSCAN, hierarchical density-based clustering, HDBSCAN*	Hiyerarşik Kümeleme, hiyerarşik kümeleme, agglomerative clustering, hierarchical agglomerative clustering
Συναφείς	3	3	4
Σύνοψη	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.	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.