Comparer des méthodes

	Analyse de regroupement	Modélisation par équations structurelles (MES)
Domaine	Statistique	Statistique
Famille	Latent structure	Latent structure
Année d'origine	1939–1967	1970
Auteur d'origine	Robert C. Tryon (early development); Ward (1963) for hierarchical; MacQueen (1967) for k-means	Karl Jöreskog (LISREL framework, 1970s)
Type	Unsupervised classification / grouping	Latent variable / causal modeling
Source fondatrice	Everitt, B. S., Landau, S., Leese, M. & Stahl, D. (2011). Cluster Analysis (5th ed.). Wiley. ISBN: 978-0470749913	Hair, J. F., Black, W. C., Babin, B. J. & Anderson, R. E. (2019). Multivariate Data Analysis (8th ed.). Cengage Learning. ISBN: 978-1473756540
Alias	clustering, unsupervised classification, data clustering, numerical taxonomy	Yapısal Eşitlik Modellemesi (SEM), structural equation modelling, covariance structure analysis, latent variable modeling
Apparentées	5	5
Résumé	Cluster analysis is a family of unsupervised multivariate techniques that partition a set of objects or observations into internally homogeneous, mutually distinct groups — clusters — based on measured characteristics, without any prior knowledge of group membership. It is widely used in market segmentation, bioinformatics, psychology, and social science to reveal natural groupings in data.	Structural equation modeling is a multivariate statistical framework that simultaneously estimates a measurement model — relating observed indicators to latent constructs — and a structural model specifying directional or reciprocal relationships among those constructs. Rooted in the LISREL tradition developed by Karl Jöreskog in the 1970s, SEM is the standard tool for testing complex theoretical models in the social, behavioural, and management sciences.