Vertaile menetelmiä

	Robust Latent Class Analysis	Ryhmäanalyysi
Tieteenala	Tilastotiede	Tilastotiede
Menetelmäperhe	Latent structure	Latent structure
Syntyvuosi	2000s	1939–1967
Kehittäjä	Building on Hennig (2004) and Vermunt & Magidson (2004)	Robert C. Tryon (early development); Ward (1963) for hierarchical; MacQueen (1967) for k-means
Tyyppi	Robust latent variable / mixture model	Unsupervised classification / grouping
Alkuperäislähde	Hennig, C. (2004). Breakdown points for maximum likelihood estimators of location-scale mixtures. Annals of Statistics, 32(4), 1313–1340.	Everitt, B. S., Landau, S., Leese, M. & Stahl, D. (2011). Cluster Analysis (5th ed.). Wiley. ISBN: 978-0470749913
Rinnakkaisnimet	robust LCA, outlier-resistant latent class analysis, trimmed-likelihood latent class analysis	clustering, unsupervised classification, data clustering, numerical taxonomy
Liittyvät	6	5
Tiivistelmä	Robust latent class analysis (robust LCA) extends the standard latent class model by incorporating outlier-resistant estimation techniques — such as trimmed likelihood, M-estimation, or downweighting — so that atypical response patterns do not distort the recovered class structure or class membership probabilities.	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.