Comparer des méthodes

	Analyse de regroupement	Analyse de classes latentes (ACL)
Domaine	Statistique	Statistique
Famille	Latent structure	Latent structure
Année d'origine	1939–1967	1950s–1968
Auteur d'origine	Robert C. Tryon (early development); Ward (1963) for hierarchical; MacQueen (1967) for k-means	Paul F. Lazarsfeld
Type	Unsupervised classification / grouping	Latent variable / person-centered classification
Source fondatrice	Everitt, B. S., Landau, S., Leese, M. & Stahl, D. (2011). Cluster Analysis (5th ed.). Wiley. ISBN: 978-0470749913	Goodman, L. A. (1974). Exploratory latent structure analysis using both identifiable and unidentifiable models. Biometrika, 61(2), 215–231.
Alias	clustering, unsupervised classification, data clustering, numerical taxonomy	LCA, latent class model, latent categorical analysis, finite mixture of multinomials
Apparentées	5	6
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.	Latent class analysis identifies unobserved subgroups — latent classes — within a population by finding patterns of responses across a set of categorical observed indicators. It is the categorical-variable counterpart of cluster analysis, but grounded in an explicit probabilistic model, and is widely used in social, health, and behavioral sciences to discover typologies in survey or diagnostic data.