השוואת שיטות

	אשכול היררכי	ניתוח גורמים	ניתוח רכיבים עיקריים
תחום	למידת מכונה	סטטיסטיקה למחקר	למידת מכונה
משפחה	Machine learning	Process / pipeline	Machine learning
שנת המקור	1963	1931	2002
הוגה השיטה	Ward, J. H.	Louis Leon Thurstone	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)
סוג	Unsupervised clustering (agglomerative)	Method	Unsupervised dimensionality reduction
מקור מכונן	Ward, J. H. (1963). Hierarchical Grouping to Optimize an Objective Function. Journal of the American Statistical Association, 58(301), 236–244.	Thurstone, L. L. (1947). Multiple Factor Analysis. University of Chicago Press.	Jolliffe, I.T. (2002). Principal Component Analysis (2nd ed.). Springer.
כינויים	Hiyerarşik Kümeleme, hiyerarşik kümeleme, agglomerative clustering, hierarchical agglomerative clustering	EFA, CFA, latent variable modeling	Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform
קשורות	4	3	3
תקציר	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.	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.	Principal Component Analysis (PCA) is an unsupervised dimensionality-reduction method — given its modern textbook treatment by Ian Jolliffe (2002) — that compresses high-dimensional data into fewer dimensions while preserving the maximum possible variance. It re-expresses correlated variables as a small set of uncorrelated principal components ordered by how much of the data's variation each one captures.