Comparar métodos

	Análisis de conglomerados	Análisis discriminante
Campo	Estadística	Estadística
Familia	Latent structure	Latent structure
Año de origen	1939–1967	1936
Autor original	Robert C. Tryon (early development); Ward (1963) for hierarchical; MacQueen (1967) for k-means	Ronald A. Fisher
Tipo	Unsupervised classification / grouping	Supervised classification and dimension reduction
Fuente seminal	Everitt, B. S., Landau, S., Leese, M. & Stahl, D. (2011). Cluster Analysis (5th ed.). Wiley. ISBN: 978-0470749913	Fisher, R. A. (1936). The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7(2), 179–188.
Alias	clustering, unsupervised classification, data clustering, numerical taxonomy	LDA, Fisher discriminant analysis, discriminant function analysis, canonical discriminant analysis
Relacionados	5	4
Resumen	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.	Discriminant analysis finds linear combinations of predictor variables that best separate two or more known groups. It is used both to understand which predictors distinguish the groups and to classify new observations into those groups with minimum error.