Methoden vergleichen

	Bayesian Clusteranalyse	Hierarchische Clusteranalyse
Fachgebiet	Statistik	Maschinelles Lernen
Familie	Latent structure	Machine learning
Entstehungsjahr	1998–2002	1963
Urheber	Fraley & Raftery (model-based); Dirichlet process formulations by Ferguson (1973) and Antoniak (1974)	Ward, J. H.
Typ	Probabilistic / model-based clustering	Unsupervised clustering (agglomerative)
Wegweisende Quelle	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.
Aliasnamen	BCA, Bayesian clustering, probabilistic cluster analysis, Bayesian model-based clustering	Hiyerarşik Kümeleme, hiyerarşik kümeleme, agglomerative clustering, hierarchical agglomerative clustering
Verwandt	6	4
Zusammenfassung	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.