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Latentná klasifikácia (LCA)×Analýza zhlukov×
OdborŠtatistikaŠtatistika
RodinaLatent structureLatent structure
Rok vzniku1950s–19681939–1967
TvorcaPaul F. LazarsfeldRobert C. Tryon (early development); Ward (1963) for hierarchical; MacQueen (1967) for k-means
TypLatent variable / person-centered classificationUnsupervised classification / grouping
Pôvodný zdrojGoodman, L. A. (1974). Exploratory latent structure analysis using both identifiable and unidentifiable models. Biometrika, 61(2), 215–231. DOI ↗Everitt, B. S., Landau, S., Leese, M. & Stahl, D. (2011). Cluster Analysis (5th ed.). Wiley. ISBN: 978-0470749913
Ďalšie názvyLCA, latent class model, latent categorical analysis, finite mixture of multinomialsclustering, unsupervised classification, data clustering, numerical taxonomy
Príbuzné65
ZhrnutieLatent 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.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.
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ScholarGatePorovnať metódy: Latent Class Analysis · Cluster Analysis. Získané 2026-06-17 z https://scholargate.app/sk/compare