Porovnat metody

	Analýza hlavních komponent	Faktorová analýza	Hierarchické shlukování
Obor	Strojové učení	Statistika ve výzkumu	Strojové učení
Rodina	Machine learning	Process / pipeline	Machine learning
Rok vzniku	2002	1931	1963
Tvůrce	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)	Louis Leon Thurstone	Ward, J. H.
Typ	Unsupervised dimensionality reduction	Method	Unsupervised clustering (agglomerative)
Původní zdroj	Jolliffe, I.T. (2002). Principal Component Analysis (2nd ed.). Springer.	Thurstone, L. L. (1947). Multiple Factor Analysis. University of Chicago Press.	Ward, J. H. (1963). Hierarchical Grouping to Optimize an Objective Function. Journal of the American Statistical Association, 58(301), 236–244.
Další názvy	Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform	EFA, CFA, latent variable modeling	Hiyerarşik Kümeleme, hiyerarşik kümeleme, agglomerative clustering, hierarchical agglomerative clustering
Příbuzné	3	3	4
Shrnutí	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.	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.	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.