Comparer des méthodes

	Regroupement hiérarchique bayésien (BHC)	Regroupement hiérarchique
Domaine	Statistique	Apprentissage automatique
Famille	Latent structure	Machine learning
Année d'origine	2005	1963
Auteur d'origine	Katherine Heller & Zoubin Ghahramani	Ward, J. H.
Type	Probabilistic clustering / model-based hierarchical agglomeration	Unsupervised clustering (agglomerative)
Source fondatrice	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.
Alias	BHC, probabilistic hierarchical clustering, Bayesian agglomerative clustering	Hiyerarşik Kümeleme, hiyerarşik kümeleme, agglomerative clustering, hierarchical agglomerative clustering
Apparentées	6	4
Résumé	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.