Comparar métodos

	Análisis de Clases Latentes (LCA)	Análisis de conglomerados
Campo	Estadística	Estadística
Familia	Latent structure	Latent structure
Año de origen	1950	1939–1967
Autor original	Paul F. Lazarsfeld	Robert C. Tryon (early development); Ward (1963) for hierarchical; MacQueen (1967) for k-means
Tipo	Latent variable / probabilistic clustering	Unsupervised classification / grouping
Fuente 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
Alias	Gizil Sınıf Analizi (LCA), latent class model, latent structure analysis	clustering, unsupervised classification, data clustering, numerical taxonomy
Relacionados	3	5
Resumen	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.