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	Bayesowska analiza skupień	Klasteryzacja hierarchiczna
Dziedzina	Statystyka	Uczenie maszynowe
Rodzina	Latent structure	Machine learning
Rok powstania	1998–2002	1963
Twórca	Fraley & Raftery (model-based); Dirichlet process formulations by Ferguson (1973) and Antoniak (1974)	Ward, J. H.
Typ	Probabilistic / model-based clustering	Unsupervised clustering (agglomerative)
Źródło pierwotne	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.
Inne nazwy	BCA, Bayesian clustering, probabilistic cluster analysis, Bayesian model-based clustering	Hiyerarşik Kümeleme, hiyerarşik kümeleme, agglomerative clustering, hierarchical agglomerative clustering
Pokrewne	6	4
Podsumowanie	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.