Comparer des méthodes

	Clustering K-means bayésien	Regroupement hiérarchique bayésien (BHC)
Domaine	Statistique	Statistique
Famille	Latent structure	Latent structure
Année d'origine	2006–2012	2005
Auteur d'origine	Kulis & Jordan (ICML 2012) formalized the Bayesian nonparametric derivation; Bishop (2006) established the variational Bayesian EM framework for Gaussian mixture models as a probabilistic foundation	Katherine Heller & Zoubin Ghahramani
Type	Probabilistic clustering / Bayesian nonparametric	Probabilistic clustering / model-based hierarchical agglomeration
Source fondatrice	Kulis, B. & Jordan, M. I. (2012). Revisiting k-means: New algorithms via Bayesian nonparametrics. In Proceedings of the 29th International Conference on Machine Learning (ICML), Edinburgh, Scotland, pp. 513–520.	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.
Alias	Bayesian K-means, probabilistic K-means, Dirichlet K-means, BKM	BHC, probabilistic hierarchical clustering, Bayesian agglomerative clustering
Apparentées	6	6
Résumé	Bayesian K-means clustering extends the classical K-means algorithm by placing prior distributions over cluster centroids and mixing proportions. This probabilistic framework provides uncertainty estimates for cluster assignments, allows principled model selection for the number of clusters, and regularises centroid estimation — especially valuable when data are scarce or high-dimensional.	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.