Σύγκριση μεθόδων

	Διαδικτυακό Μοντέλο Μείγματος Gaussian	Διαδικτυακός K-means
Πεδίο	Μηχανική Μάθηση	Μηχανική Μάθηση
Οικογένεια	Machine learning	Machine learning
Έτος προέλευσης	2000–2009	1967 (online update rule); 2010 (mini-batch variant)
Δημιουργός	Cappé, O. & Moulines, E. (online EM formulation)	MacQueen, J. (batch); Sculley, D. (mini-batch web-scale variant)
Τύπος	Probabilistic clustering / density estimation (incremental)	Unsupervised clustering (online/streaming)
Θεμελιώδης πηγή	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.
Εναλλακτικές ονομασίες	Online GMM, Incremental GMM, Streaming Gaussian Mixture Model, Sequential GMM	sequential k-means, streaming k-means, incremental k-means, online clustering
Συναφείς	5	4
Σύνοψη	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.