Comparar métodos

	Agrupamiento jerárquico	Modelo de Mezcla Gaussiana	Análisis de Componentes Principales
Campo	Aprendizaje automático	Aprendizaje automático	Aprendizaje automático
Familia	Machine learning	Machine learning	Machine learning
Año de origen	1963	1977	2002
Autor original	Ward, J. H.	Dempster, Laird & Rubin (EM algorithm)	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)
Tipo	Unsupervised clustering (agglomerative)	Probabilistic (soft) clustering — mixture model	Unsupervised dimensionality reduction
Fuente seminal	Ward, J. H. (1963). Hierarchical Grouping to Optimize an Objective Function. Journal of the American Statistical Association, 58(301), 236–244.	Dempster, A.P., Laird, N.M. & Rubin, D.B. (1977). Maximum Likelihood from Incomplete Data via the EM Algorithm. Journal of the Royal Statistical Society: Series B, 39(1), 1–22.	Jolliffe, I.T. (2002). Principal Component Analysis (2nd ed.). Springer.
Alias	Hiyerarşik Kümeleme, hiyerarşik kümeleme, agglomerative clustering, hierarchical agglomerative clustering	Gaussian Karışım Modeli (GMM Kümeleme), GMM, GMM clustering, mixture of Gaussians	Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform
Relacionados	4	4	3
Resumen	Hierarchical clustering is an unsupervised method that groups observations into nested clusters and draws the result as a dendrogram, so the number of clusters need not be fixed in advance. Its agglomerative form rests on the objective-function grouping criterion introduced by Joe Ward in 1963.	A Gaussian Mixture Model is a probabilistic clustering method that models the data as a weighted mixture of several Gaussian distributions, fitted with the Expectation–Maximization algorithm formalized by Dempster, Laird & Rubin in 1977. It is a generalization of K-means in which each cluster can take its own shape, size, and orientation.	Principal Component Analysis (PCA) is an unsupervised dimensionality-reduction method — given its modern textbook treatment by Ian Jolliffe (2002) — that compresses high-dimensional data into fewer dimensions while preserving the maximum possible variance. It re-expresses correlated variables as a small set of uncorrelated principal components ordered by how much of the data's variation each one captures.