Sammenlign metoder

	Online Gaussian Mixture Model	Online K-means
Fagområde	Maskinlæring	Maskinlæring
Familie	Machine learning	Machine learning
Oprindelsesår	2000–2009	1967 (online update rule); 2010 (mini-batch variant)
Ophavsperson	Cappé, O. & Moulines, E. (online EM formulation)	MacQueen, J. (batch); Sculley, D. (mini-batch web-scale variant)
Type	Probabilistic clustering / density estimation (incremental)	Unsupervised clustering (online/streaming)
Oprindelig kilde	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.
Aliasser	Online GMM, Incremental GMM, Streaming Gaussian Mixture Model, Sequential GMM	sequential k-means, streaming k-means, incremental k-means, online clustering
Relaterede	5	4
Resumé	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.