Comparar métodos

	Agrupamento Hierárquico	Análise de Componentes Principais
Área	Aprendizado de máquina	Aprendizado de máquina
Família	Machine learning	Machine learning
Ano de origem	1963	2002
Autor original	Ward, J. H.	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)
Tipo	Unsupervised clustering (agglomerative)	Unsupervised dimensionality reduction
Fonte seminal	Ward, J. H. (1963). Hierarchical Grouping to Optimize an Objective Function. Journal of the American Statistical Association, 58(301), 236–244.	Jolliffe, I.T. (2002). Principal Component Analysis (2nd ed.). Springer.
Outros nomes	Hiyerarşik Kümeleme, hiyerarşik kümeleme, agglomerative clustering, hierarchical agglomerative clustering	Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform
Relacionados	4	3
Resumo	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.	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.