Methoden vergleichen

	Bayessches Gaußsches Mischmodell	K-means Clustering
Fachgebiet	Maschinelles Lernen	Maschinelles Lernen
Familie	Machine learning	Machine learning
Entstehungsjahr	1999–2006	1967 (formalized 1982)
Urheber	Attias, H.; Bishop, C. M.	MacQueen, J. B.; Lloyd, S. P.
Typ	Probabilistic clustering / density estimation	Partitional clustering
Wegweisende Quelle	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.
Aliasnamen	Bayesian GMM, Variational Gaussian Mixture, VBGMM, Dirichlet Process Gaussian Mixture	k-means clustering, Lloyd's algorithm, k-means partitioning, hard k-means
Verwandt	4	4
Zusammenfassung	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.