Comparer des méthodes

	Modèle gaussien de mélange en ligne	Regroupement par K-moyennes
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2000–2009	1967 (formalized 1982)
Auteur d'origine	Cappé, O. & Moulines, E. (online EM formulation)	MacQueen, J. B.; Lloyd, S. P.
Type	Probabilistic clustering / density estimation (incremental)	Partitional clustering
Source fondatrice	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.	Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129–137.
Alias	Online GMM, Incremental GMM, Streaming Gaussian Mixture Model, Sequential GMM	k-means clustering, Lloyd's algorithm, k-means partitioning, hard k-means
Apparentées	5	4
Résumé	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.	K-means is a classic unsupervised partitional clustering algorithm that divides a dataset into K non-overlapping groups by iteratively assigning each observation to its nearest centroid and updating centroids as the mean of their assigned points. It is one of the most widely used exploratory tools in machine learning and data analysis.