Comparer des méthodes

	DBSCAN	Modèle gaussien de mélange en ligne	K-means en ligne
Domaine	Apprentissage automatique	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning	Machine learning
Année d'origine	1996	2000–2009	1967 (online update rule); 2010 (mini-batch variant)
Auteur d'origine	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)
Type	Density-based clustering algorithm	Probabilistic clustering / density estimation (incremental)	Unsupervised clustering (online/streaming)
Source fondatrice	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.
Alias	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
Apparentées	3	5	4
Résumé	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.