Comparer des méthodes

	Clustering K-means bayésien	Modélisation par mélange
Domaine	Statistique	Statistique
Famille	Latent structure	Latent structure
Année d'origine	2006–2012	1894
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	Karl Pearson
Type	Probabilistic clustering / Bayesian nonparametric	Latent variable / density estimation
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.	McLachlan, G. J. & Peel, D. (2000). Finite Mixture Models. Wiley-Interscience. ISBN: 978-0471006268
Alias	Bayesian K-means, probabilistic K-means, Dirichlet K-means, BKM	finite mixture model, mixture distribution model, FMM, model-based 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.	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.