Methoden vergleichen

	Faktorenanalyse	K-means Clustering	Hauptkomponentenanalyse
Fachgebiet	Forschungsstatistik	Maschinelles Lernen	Maschinelles Lernen
Familie	Process / pipeline	Machine learning	Machine learning
Entstehungsjahr	1931	1967 (formalized 1982)	2002
Urheber	Louis Leon Thurstone	MacQueen, J. B.; Lloyd, S. P.	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)
Typ	Method	Partitional clustering	Unsupervised dimensionality reduction
Wegweisende Quelle	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.	Jolliffe, I.T. (2002). Principal Component Analysis (2nd ed.). Springer.
Aliasnamen	EFA, CFA, latent variable modeling	k-means clustering, Lloyd's algorithm, k-means partitioning, hard k-means	Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform
Verwandt	3	4	3
Zusammenfassung	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.	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.