Compara mètodes

	Anàlisi de Classes Latents (LCA)	Anàlisi de clústers
Camp	Estadística	Estadística
Família	Latent structure	Latent structure
Any d'origen	1950	1939–1967
Autor original	Paul F. Lazarsfeld	Robert C. Tryon (early development); Ward (1963) for hierarchical; MacQueen (1967) for k-means
Tipus	Latent variable / probabilistic clustering	Unsupervised classification / grouping
Font seminal	Hagenaars, J. A. & McCutcheon, A. L. (Eds.) (2002). Applied Latent Class Analysis. Cambridge University Press. ISBN: 978-0521594516	Everitt, B. S., Landau, S., Leese, M. & Stahl, D. (2011). Cluster Analysis (5th ed.). Wiley. ISBN: 978-0470749913
Àlies	Gizil Sınıf Analizi (LCA), latent class model, latent structure analysis	clustering, unsupervised classification, data clustering, numerical taxonomy
Relacionats	3	5
Resum	Latent class analysis is a probabilistic model-based clustering technique that identifies unobserved subgroups — latent classes — within a population on the basis of patterns of categorical, binary, or ordinal indicator responses. Originating in sociological measurement theory with Lazarsfeld's latent structure work around 1950 and formalised computationally by Goodman in the 1970s, it is widely used in the social, health, and behavioural sciences to reveal hidden population heterogeneity.	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.