Comparer des méthodes

	DBSCAN en ligne	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	1998	2000–2009	1967 (online update rule); 2010 (mini-batch variant)
Auteur d'origine	Ester, M., Kriegel, H.-P., Sander, J., Wimmer, M., & Xu, X.	Cappé, O. & Moulines, E. (online EM formulation)	MacQueen, J. (batch); Sculley, D. (mini-batch web-scale variant)
Type	Incremental density-based clustering	Probabilistic clustering / density estimation (incremental)	Unsupervised clustering (online/streaming)
Source fondatrice	Ester, M., Kriegel, H.-P., Sander, J., Wimmer, M., & Xu, X. (1998). Incremental Clustering for Mining in a Data Warehousing Environment. In Proceedings of the 24th International Conference on Very Large Data Bases (VLDB), pp. 323–333.	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	Incremental DBSCAN, Streaming DBSCAN, Online density-based clustering, iDBSCAN	Online GMM, Incremental GMM, Streaming Gaussian Mixture Model, Sequential GMM	sequential k-means, streaming k-means, incremental k-means, online clustering
Apparentées	5	5	4
Résumé	Online DBSCAN extends the classic density-based clustering algorithm to handle continuously arriving data points without re-clustering the entire dataset from scratch. Each new observation is integrated into the existing cluster structure by local neighborhood queries, making it practical for streaming and data-warehousing scenarios where data grows incrementally.	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.