Comparer des méthodes

	Modèle de mélange gaussien explicable	Analyse de classes latentes (ACL)
Domaine	Apprentissage automatique	Statistique
Famille	Machine learning	Latent structure
Année d'origine	1995–2020s	1950s–1968
Auteur d'origine	Reynolds, D. A. & Rose, R. C. (GMM); explainability extensions by various authors	Paul F. Lazarsfeld
Type	Probabilistic clustering with post-hoc or built-in explainability	Latent variable / person-centered classification
Source fondatrice	Murphy, K. P. (2012). Machine Learning: A Probabilistic Perspective (Ch. 11 — Mixture Models). MIT Press. ISBN: 978-0-262-01802-9	Goodman, L. A. (1974). Exploratory latent structure analysis using both identifiable and unidentifiable models. Biometrika, 61(2), 215–231.
Alias	X-GMM, Interpretable GMM, Explainable GMM, Transparent Gaussian Mixture Model	LCA, latent class model, latent categorical analysis, finite mixture of multinomials
Apparentées	3	6
Résumé	An Explainable Gaussian Mixture Model (X-GMM) augments the classical GMM probabilistic clustering framework with transparency mechanisms — such as feature-attribution scores, component-level summaries, or sparse covariance structures — so that discovered clusters and density estimates can be understood, communicated, and audited by human experts.	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.