विधियों की तुलना करें

	अव्यक्त वर्ग विश्लेषण (Latent Class Analysis - LCA)	समूह विश्लेषण
क्षेत्र	सांख्यिकी	सांख्यिकी
परिवार	Latent structure	Latent structure
उद्भव वर्ष	1950s–1968	1939–1967
प्रवर्तक	Paul F. Lazarsfeld	Robert C. Tryon (early development); Ward (1963) for hierarchical; MacQueen (1967) for k-means
प्रकार	Latent variable / person-centered classification	Unsupervised classification / grouping
मौलिक स्रोत	Goodman, L. A. (1974). Exploratory latent structure analysis using both identifiable and unidentifiable models. Biometrika, 61(2), 215–231.	Everitt, B. S., Landau, S., Leese, M. & Stahl, D. (2011). Cluster Analysis (5th ed.). Wiley. ISBN: 978-0470749913
उपनाम	LCA, latent class model, latent categorical analysis, finite mixture of multinomials	clustering, unsupervised classification, data clustering, numerical taxonomy
संबंधित	6	5
सारांश	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.	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.