Comparar métodos

	Análisis de conglomerados	Modelado de mezclas
Campo	Estadística	Estadística
Familia	Latent structure	Latent structure
Año de origen	1939–1967	1894
Autor original	Robert C. Tryon (early development); Ward (1963) for hierarchical; MacQueen (1967) for k-means	Karl Pearson
Tipo	Unsupervised classification / grouping	Latent variable / density estimation
Fuente seminal	Everitt, B. S., Landau, S., Leese, M. & Stahl, D. (2011). Cluster Analysis (5th ed.). Wiley. ISBN: 978-0470749913	McLachlan, G. J. & Peel, D. (2000). Finite Mixture Models. Wiley-Interscience. ISBN: 978-0471006268
Alias	clustering, unsupervised classification, data clustering, numerical taxonomy	finite mixture model, mixture distribution model, FMM, model-based clustering
Relacionados	5	6
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.	Mixture modeling assumes that a population is composed of K unobserved subpopulations, each described by its own probability distribution. The observed data are treated as draws from a weighted combination of these component distributions. It provides a principled, model-based alternative to ad hoc clustering and supports formal comparison of solutions with different numbers of components.