Comparer des méthodes

	Regroupement par K-moyennes	Regroupement hiérarchique	Analyse Discriminante Linéaire (ADL	Forêt Aléatoire
Domaine	Apprentissage automatique	Apprentissage automatique	Statistique	Apprentissage automatique
Famille	Machine learning	Machine learning	Hypothesis test	Machine learning
Année d'origine	1967	1963	1936	2001
Auteur d'origine	MacQueen, J.	Ward, J. H.	Ronald A. Fisher	Breiman, L.
Type	Partitional clustering (centroid-based)	Unsupervised clustering (agglomerative)	Parametric linear classifier / dimensionality reduction	Ensemble (bagging of decision trees)
Source fondatrice	MacQueen, J. (1967). Some Methods for Classification and Analysis of Multivariate Observations. Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, 1, 281–297.	Ward, J. H. (1963). Hierarchical Grouping to Optimize an Objective Function. Journal of the American Statistical Association, 58(301), 236–244.	Fisher, R.A. (1936). The Use of Multiple Measurements in Taxonomic Problems. Annals of Eugenics, 7(2), 179–188.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
Alias	K-Ortalamalar Kümeleme, k-ortalamalar kümeleme, k-means, centroid clustering	Hiyerarşik Kümeleme, hiyerarşik kümeleme, agglomerative clustering, hierarchical agglomerative clustering	LDA, Fisher's LDA, Fisher's linear discriminant, discriminant function analysis	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Apparentées	3	4	7	4
Résumé	K-Means Clustering is a centroid-based partitional clustering algorithm, traced to J. MacQueen in 1967, that splits data into k clusters by assigning each observation to its nearest cluster centre. It is widely used for marketing segmentation, customer grouping, and exploratory analysis.	Hierarchical clustering is an unsupervised method that groups observations into nested clusters and draws the result as a dendrogram, so the number of clusters need not be fixed in advance. Its agglomerative form rests on the objective-function grouping criterion introduced by Joe Ward in 1963.	Linear Discriminant Analysis (LDA) is a parametric supervised classification method that finds the linear combination of continuous predictors that best separates two or more predefined groups. Introduced by Ronald A. Fisher in his landmark 1936 paper on taxonomic measurements, it simultaneously serves as a classifier and a dimensionality-reduction tool, and can be understood as the classification-oriented counterpart of MANOVA.	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.