Σύγκριση μεθόδων

	ΟΠΤΙΚΗ	HDBSCAN
Πεδίο	Μηχανική Μάθηση	Μηχανική Μάθηση
Οικογένεια	Machine learning	Machine learning
Έτος προέλευσης	1999	2013
Δημιουργός	Ankerst, M.; Breunig, M. M.; Kriegel, H.-P.; Sander, J.	Campello, R. J. G. B.; Moulavi, D.; Sander, J.
Τύπος	Density-based clustering (reachability ordering)	Hierarchical density-based clustering
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.
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*
Συναφείς	3	3
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.