Methoden vergelijken

	Bayesiaanse Clusteranalyse	Hiërarchische clustering
Vakgebied	Statistiek	Machine learning
Familie	Latent structure	Machine learning
Jaar van ontstaan	1998–2002	1963
Grondlegger	Fraley & Raftery (model-based); Dirichlet process formulations by Ferguson (1973) and Antoniak (1974)	Ward, J. H.
Type	Probabilistic / model-based clustering	Unsupervised clustering (agglomerative)
Oorspronkelijke bron	Fraley, C. & Raftery, A. E. (2002). Model-based clustering, discriminant analysis, and density estimation. Journal of the American Statistical Association, 97(458), 611–631.	Ward, J. H. (1963). Hierarchical Grouping to Optimize an Objective Function. Journal of the American Statistical Association, 58(301), 236–244.
Aliassen	BCA, Bayesian clustering, probabilistic cluster analysis, Bayesian model-based clustering	Hiyerarşik Kümeleme, hiyerarşik kümeleme, agglomerative clustering, hierarchical agglomerative clustering
Verwant	6	4
Samenvatting	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.	Hierarchical clustering is an unsupervised method that groups observations into nested clusters and draws the result as a dendrogram, so the number of clusters need not be fixed in advance. Its agglomerative form rests on the objective-function grouping criterion introduced by Joe Ward in 1963.