Vertaile menetelmiä

	Online Gaussian Mixture Model	K-means-klusterointi
Tieteenala	Koneoppiminen	Koneoppiminen
Menetelmäperhe	Machine learning	Machine learning
Syntyvuosi	2000–2009	1967 (formalized 1982)
Kehittäjä	Cappé, O. & Moulines, E. (online EM formulation)	MacQueen, J. B.; Lloyd, S. P.
Tyyppi	Probabilistic clustering / density estimation (incremental)	Partitional clustering
Alkuperäislähde	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.
Rinnakkaisnimet	Online GMM, Incremental GMM, Streaming Gaussian Mixture Model, Sequential GMM	k-means clustering, Lloyd's algorithm, k-means partitioning, hard k-means
Liittyvät	5	4
Tiivistelmä	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.