Comparer des méthodes

	Clustering K-means bayésien	Analyse de classes latentes (ACL)
Domaine	Statistique	Statistique
Famille	Latent structure	Latent structure
Année d'origine	2006–2012	1950s–1968
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	Paul F. Lazarsfeld
Type	Probabilistic clustering / Bayesian nonparametric	Latent variable / person-centered classification
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.	Goodman, L. A. (1974). Exploratory latent structure analysis using both identifiable and unidentifiable models. Biometrika, 61(2), 215–231.
Alias	Bayesian K-means, probabilistic K-means, Dirichlet K-means, BKM	LCA, latent class model, latent categorical analysis, finite mixture of multinomials
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.	Latent class analysis identifies unobserved subgroups — latent classes — within a population by finding patterns of responses across a set of categorical observed indicators. It is the categorical-variable counterpart of cluster analysis, but grounded in an explicit probabilistic model, and is widely used in social, health, and behavioral sciences to discover typologies in survey or diagnostic data.