Comparer des méthodes

	Regroupement hiérarchique bayésien (BHC)	Analyse de regroupement
Domaine	Statistique	Statistique
Famille	Latent structure	Latent structure
Année d'origine	2005	1939–1967
Auteur d'origine	Katherine Heller & Zoubin Ghahramani	Robert C. Tryon (early development); Ward (1963) for hierarchical; MacQueen (1967) for k-means
Type	Probabilistic clustering / model-based hierarchical agglomeration	Unsupervised classification / grouping
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.	Everitt, B. S., Landau, S., Leese, M. & Stahl, D. (2011). Cluster Analysis (5th ed.). Wiley. ISBN: 978-0470749913
Alias	BHC, probabilistic hierarchical clustering, Bayesian agglomerative clustering	clustering, unsupervised classification, data clustering, numerical taxonomy
Apparentées	6	5
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.	Cluster analysis is a family of unsupervised multivariate techniques that partition a set of objects or observations into internally homogeneous, mutually distinct groups — clusters — based on measured characteristics, without any prior knowledge of group membership. It is widely used in market segmentation, bioinformatics, psychology, and social science to reveal natural groupings in data.