Vertaile menetelmiä

	Bayesiläinen Gaussinen sekoitusmalli	K-means-klusterointi
Tieteenala	Koneoppiminen	Koneoppiminen
Menetelmäperhe	Machine learning	Machine learning
Syntyvuosi	1999–2006	1967 (formalized 1982)
Kehittäjä	Attias, H.; Bishop, C. M.	MacQueen, J. B.; Lloyd, S. P.
Tyyppi	Probabilistic clustering / density estimation	Partitional clustering
Alkuperäislähde	Bishop, C. M. (2006). Pattern Recognition and Machine Learning (Ch. 10). Springer. ISBN: 978-0-387-31073-2	Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129–137.
Rinnakkaisnimet	Bayesian GMM, Variational Gaussian Mixture, VBGMM, Dirichlet Process Gaussian Mixture	k-means clustering, Lloyd's algorithm, k-means partitioning, hard k-means
Liittyvät	4	4
Tiivistelmä	The Bayesian Gaussian Mixture Model places prior distributions over all mixture parameters and infers their posteriors — typically via Variational Bayes or MCMC — rather than fitting fixed point estimates. This yields principled uncertainty quantification, automatic selection of the effective number of components, and resistance to overfitting small datasets.	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.