Porovnat metody

	HDBSCAN	Online K-means
Obor	Strojové učení	Strojové učení
Rodina	Machine learning	Machine learning
Rok vzniku	2013	1967 (online update rule); 2010 (mini-batch variant)
Tvůrce	Campello, R. J. G. B.; Moulavi, D.; Sander, J.	MacQueen, J. (batch); Sculley, D. (mini-batch web-scale variant)
Typ	Hierarchical density-based clustering	Unsupervised clustering (online/streaming)
Původní zdroj	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.	MacQueen, J. (1967). Some methods for classification and analysis of multivariate observations. In Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, Vol. 1, pp. 281–297. University of California Press.
Další názvy	HDBSCAN, Hierarchical DBSCAN, hierarchical density-based clustering, HDBSCAN*	sequential k-means, streaming k-means, incremental k-means, online clustering
Příbuzné	3	4
Shrnutí	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.	Online K-means is a streaming variant of the classical K-means algorithm that updates cluster centroids one observation at a time — or in small mini-batches — without storing the entire dataset in memory. It is particularly suited to large-scale, real-time, or continuously arriving data where batch recomputation would be too slow or impractical.