Compară metode

	Clustering K-Means	Analiza Discriminantă Liniară (ADL	Pădurea Aleatoare (Random Forest)
Domeniu	Învățare automată	Statistică	Învățare automată
Familie	Machine learning	Hypothesis test	Machine learning
Anul apariției	1967	1936	2001
Autorul original	MacQueen, J.	Ronald A. Fisher	Breiman, L.
Tip	Partitional clustering (centroid-based)	Parametric linear classifier / dimensionality reduction	Ensemble (bagging of decision trees)
Sursa seminală	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.	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.
Denumiri alternative	K-Ortalamalar Kümeleme, k-ortalamalar kümeleme, k-means, centroid clustering	LDA, Fisher's LDA, Fisher's linear discriminant, discriminant function analysis	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Înrudite	3	7	4
Rezumat	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.	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.