Comparer des méthodes

	Regroupement hiérarchique bayésien (BHC)	Modélisation par mélange
Domaine	Statistique	Statistique
Famille	Latent structure	Latent structure
Année d'origine	2005	1894
Auteur d'origine	Katherine Heller & Zoubin Ghahramani	Karl Pearson
Type	Probabilistic clustering / model-based hierarchical agglomeration	Latent variable / density estimation
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.	McLachlan, G. J. & Peel, D. (2000). Finite Mixture Models. Wiley-Interscience. ISBN: 978-0471006268
Alias	BHC, probabilistic hierarchical clustering, Bayesian agglomerative clustering	finite mixture model, mixture distribution model, FMM, model-based clustering
Apparentées	6	6
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.	Mixture modeling assumes that a population is composed of K unobserved subpopulations, each described by its own probability distribution. The observed data are treated as draws from a weighted combination of these component distributions. It provides a principled, model-based alternative to ad hoc clustering and supports formal comparison of solutions with different numbers of components.