Methoden vergelijken

	HDBSCAN	Online Gaussisch Mengselmodel
Vakgebied	Machine learning	Machine learning
Familie	Machine learning	Machine learning
Jaar van ontstaan	2013	2000–2009
Grondlegger	Campello, R. J. G. B.; Moulavi, D.; Sander, J.	Cappé, O. & Moulines, E. (online EM formulation)
Type	Hierarchical density-based clustering	Probabilistic clustering / density estimation (incremental)
Oorspronkelijke bron	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.	Cappé, O. & Moulines, E. (2009). On-line expectation-maximization algorithm for latent data models. Journal of the Royal Statistical Society: Series B, 71(3), 593–613.
Aliassen	HDBSCAN, Hierarchical DBSCAN, hierarchical density-based clustering, HDBSCAN*	Online GMM, Incremental GMM, Streaming Gaussian Mixture Model, Sequential GMM
Verwant	3	5
Samenvatting	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 Gaussian Mixture Model adapts the classic GMM to streaming or large-scale data by replacing full-batch EM with incremental updates — processing one observation or mini-batch at a time and continuously refining component means, covariances, and mixing weights without revisiting the entire dataset.