Comparer des méthodes

	Analyse de classification bayésienne	Modélisation par mélange
Domaine	Statistique	Statistique
Famille	Latent structure	Latent structure
Année d'origine	1998–2002	1894
Auteur d'origine	Fraley & Raftery (model-based); Dirichlet process formulations by Ferguson (1973) and Antoniak (1974)	Karl Pearson
Type	Probabilistic / model-based clustering	Latent variable / density estimation
Source fondatrice	Fraley, C. & Raftery, A. E. (2002). Model-based clustering, discriminant analysis, and density estimation. Journal of the American Statistical Association, 97(458), 611–631.	McLachlan, G. J. & Peel, D. (2000). Finite Mixture Models. Wiley-Interscience. ISBN: 978-0471006268
Alias	BCA, Bayesian clustering, probabilistic cluster analysis, Bayesian model-based clustering	finite mixture model, mixture distribution model, FMM, model-based clustering
Apparentées	6	6
Résumé	Bayesian cluster analysis assigns observations to latent groups by combining a probabilistic model of within-cluster data with prior beliefs about cluster parameters and the number of clusters. It yields posterior probabilities of cluster membership and principled uncertainty estimates, making it more transparent than classical distance-based clustering algorithms.	Mixture modeling assumes that a population is composed of K unobserved subpopulations, each described by its own probability distribution. The observed data are treated as draws from a weighted combination of these component distributions. It provides a principled, model-based alternative to ad hoc clustering and supports formal comparison of solutions with different numbers of components.