Comparer des méthodes

	Analyse de classification bayésienne	Analyse de regroupement
Domaine	Statistique	Statistique
Famille	Latent structure	Latent structure
Année d'origine	1998–2002	1939–1967
Auteur d'origine	Fraley & Raftery (model-based); Dirichlet process formulations by Ferguson (1973) and Antoniak (1974)	Robert C. Tryon (early development); Ward (1963) for hierarchical; MacQueen (1967) for k-means
Type	Probabilistic / model-based clustering	Unsupervised classification / grouping
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.	Everitt, B. S., Landau, S., Leese, M. & Stahl, D. (2011). Cluster Analysis (5th ed.). Wiley. ISBN: 978-0470749913
Alias	BCA, Bayesian clustering, probabilistic cluster analysis, Bayesian model-based clustering	clustering, unsupervised classification, data clustering, numerical taxonomy
Apparentées	6	5
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.	Cluster analysis is a family of unsupervised multivariate techniques that partition a set of objects or observations into internally homogeneous, mutually distinct groups — clusters — based on measured characteristics, without any prior knowledge of group membership. It is widely used in market segmentation, bioinformatics, psychology, and social science to reveal natural groupings in data.