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Klusteranalys×Latent Class Analysis (LCA)×
ÄmnesområdeStatistikStatistik
FamiljLatent structureLatent structure
Ursprungsår1939–19671950s–1968
UpphovspersonRobert C. Tryon (early development); Ward (1963) for hierarchical; MacQueen (1967) for k-meansPaul F. Lazarsfeld
TypUnsupervised classification / groupingLatent variable / person-centered classification
UrsprungskällaEveritt, B. S., Landau, S., Leese, M. & Stahl, D. (2011). Cluster Analysis (5th ed.). Wiley. ISBN: 978-0470749913Goodman, L. A. (1974). Exploratory latent structure analysis using both identifiable and unidentifiable models. Biometrika, 61(2), 215–231. DOI ↗
Aliasclustering, unsupervised classification, data clustering, numerical taxonomyLCA, latent class model, latent categorical analysis, finite mixture of multinomials
Närliggande56
SammanfattningCluster 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.
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ScholarGateJämför metoder: Cluster Analysis · Latent Class Analysis. Hämtad 2026-06-17 från https://scholargate.app/sv/compare