Confronta i metodi

	Modello Bayesiano a Mischia di Gaussiane	Clustering K-means
Campo	Apprendimento automatico	Apprendimento automatico
Famiglia	Machine learning	Machine learning
Anno di origine	1999–2006	1967 (formalized 1982)
Ideatore	Attias, H.; Bishop, C. M.	MacQueen, J. B.; Lloyd, S. P.
Tipo	Probabilistic clustering / density estimation	Partitional clustering
Fonte seminale	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.
Alias	Bayesian GMM, Variational Gaussian Mixture, VBGMM, Dirichlet Process Gaussian Mixture	k-means clustering, Lloyd's algorithm, k-means partitioning, hard k-means
Correlati	4	4
Sintesi	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.