Comparer des méthodes

	Analyse factorielle	Regroupement par K-moyennes	Forêt Aléatoire
Domaine	Statistiques de recherche	Apprentissage automatique	Apprentissage automatique
Famille	Process / pipeline	Machine learning	Machine learning
Année d'origine	1931	1967 (formalized 1982)	2001
Auteur d'origine	Louis Leon Thurstone	MacQueen, J. B.; Lloyd, S. P.	Breiman, L.
Type	Method	Partitional clustering	Ensemble (bagging of decision trees)
Source fondatrice	Thurstone, L. L. (1947). Multiple Factor Analysis. University of Chicago Press.	Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129–137.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
Alias	EFA, CFA, latent variable modeling	k-means clustering, Lloyd's algorithm, k-means partitioning, hard k-means	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Apparentées	3	4	4
Résumé	Factor analysis is a statistical technique for identifying latent (unobserved) dimensions underlying observed variables, developed by Louis Leon Thurstone in the 1930s and formalized by Jöreskog (1969). Exploratory factor analysis (EFA) discovers unknown factor structure from data; confirmatory factor analysis (CFA) tests hypothesized relationships between observed and latent variables. Essential in psychometrics (test development), organizational research (measuring constructs like leadership style), and biomedicine (identifying disease subtypes), factor analysis reduces dimensionality while revealing conceptual organization in multivariate data.	K-means is a classic unsupervised partitional clustering algorithm that divides a dataset into K non-overlapping groups by iteratively assigning each observation to its nearest centroid and updating centroids as the mean of their assigned points. It is one of the most widely used exploratory tools in machine learning and data analysis.	Random Forest is an ensemble learning method, introduced by Leo Breiman in 2001, that grows many decision trees on bootstrap samples of the data and combines their votes to produce strong classification and regression. By pooling many slightly different trees, it produces more accurate and more stable predictions than any single tree.