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	DBSCAN	Online'owy model mieszaniny Gaussa	Online K-means
Dziedzina	Uczenie maszynowe	Uczenie maszynowe	Uczenie maszynowe
Rodzina	Machine learning	Machine learning	Machine learning
Rok powstania	1996	2000–2009	1967 (online update rule); 2010 (mini-batch variant)
Twórca	Ester, M., Kriegel, H.-P., Sander, J. & Xu, X.	Cappé, O. & Moulines, E. (online EM formulation)	MacQueen, J. (batch); Sculley, D. (mini-batch web-scale variant)
Typ	Density-based clustering algorithm	Probabilistic clustering / density estimation (incremental)	Unsupervised clustering (online/streaming)
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.	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.	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.
Inne nazwy	DBSCAN Kümeleme, density-based clustering, density-based spatial clustering	Online GMM, Incremental GMM, Streaming Gaussian Mixture Model, Sequential GMM	sequential k-means, streaming k-means, incremental k-means, online clustering
Pokrewne	3	5	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.	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.	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.