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	Hierarchiczne klastrowanie bayesowskie (BHC)	Klasteryzacja hierarchiczna
Dziedzina	Statystyka	Uczenie maszynowe
Rodzina	Latent structure	Machine learning
Rok powstania	2005	1963
Twórca	Katherine Heller & Zoubin Ghahramani	Ward, J. H.
Typ	Probabilistic clustering / model-based hierarchical agglomeration	Unsupervised clustering (agglomerative)
Źródło pierwotne	Heller, K. A. & Ghahramani, Z. (2005). Bayesian hierarchical clustering. In Proceedings of the 22nd International Conference on Machine Learning (ICML 2005), pp. 297–304. ACM.	Ward, J. H. (1963). Hierarchical Grouping to Optimize an Objective Function. Journal of the American Statistical Association, 58(301), 236–244.
Inne nazwy	BHC, probabilistic hierarchical clustering, Bayesian agglomerative clustering	Hiyerarşik Kümeleme, hiyerarşik kümeleme, agglomerative clustering, hierarchical agglomerative clustering
Pokrewne	6	4
Podsumowanie	Bayesian hierarchical clustering is a probabilistic agglomerative algorithm that builds a tree of nested cluster merges using Bayesian model comparison at each step. Rather than minimising a geometric linkage criterion, it evaluates at every candidate merge whether the data from two clusters are better explained by a single combined model or by two separate models, yielding a statistically principled dendrogram.	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.