方法对比

	层次聚类	因子分析	高斯混合模型
领域	机器学习	研究统计学	机器学习
方法族	Machine learning	Process / pipeline	Machine learning
起源年份	1963	1931	1977
提出者	Ward, J. H.	Louis Leon Thurstone	Dempster, Laird & Rubin (EM algorithm)
类型	Unsupervised clustering (agglomerative)	Method	Probabilistic (soft) clustering — mixture model
开创性文献	Ward, J. H. (1963). Hierarchical Grouping to Optimize an Objective Function. Journal of the American Statistical Association, 58(301), 236–244.	Thurstone, L. L. (1947). Multiple Factor Analysis. University of Chicago Press.	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.
别名	Hiyerarşik Kümeleme, hiyerarşik kümeleme, agglomerative clustering, hierarchical agglomerative clustering	EFA, CFA, latent variable modeling	Gaussian Karışım Modeli (GMM Kümeleme), GMM, GMM clustering, mixture of Gaussians
相关	4	3	4
摘要	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.	Factor analysis is a statistical technique for identifying latent (unobserved) dimensions underlying observed variables, developed by Louis Leon Thurstone in the 1930s and formalized by Jöreskog (1969). Exploratory factor analysis (EFA) discovers unknown factor structure from data; confirmatory factor analysis (CFA) tests hypothesized relationships between observed and latent variables. Essential in psychometrics (test development), organizational research (measuring constructs like leadership style), and biomedicine (identifying disease subtypes), factor analysis reduces dimensionality while revealing conceptual organization in multivariate data.	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.