Comparer des méthodes

	Ensemble K-means	Modèle de Mélange Gaussien en Ensemble
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2002	2000s
Auteur d'origine	Strehl, A. & Ghosh, J.	Combination of GMM (Dempster et al., 1977) and ensemble learning (Dietterich, 2000)
Type	Ensemble clustering (consensus aggregation of K-means partitions)	Ensemble of probabilistic generative models
Source fondatrice	Strehl, A. & Ghosh, J. (2002). Cluster ensembles — a knowledge reuse framework for combining multiple partitions. Journal of Machine Learning Research, 3, 583–617.	Bishop, C. M. (2006). Pattern Recognition and Machine Learning (Ch. 9: Mixture Models and EM). Springer. ISBN: 978-0-387-31073-2
Alias	consensus K-means, K-means ensemble clustering, cluster ensemble with K-means, EKM	E-GMM, GMM ensemble, mixture model ensemble, ensemble GMM
Apparentées	3	4
Résumé	Ensemble K-means runs K-means clustering many times under varied initializations, random seeds, or feature subsets, then aggregates the resulting partitions into a single consensus assignment. This approach reduces K-means' well-known sensitivity to initialization and produces more stable, reproducible clusters than any single run.	Ensemble Gaussian Mixture Model (E-GMM) combines multiple independently fitted Gaussian Mixture Models to improve density estimation, clustering stability, and anomaly detection. By averaging or aggregating the probabilistic outputs of several GMMs — each trained on a different data subset or random initialization — the ensemble reduces sensitivity to local optima and random seed choice, yielding more robust and reliable results than any single GMM.