Confronta i metodi

	Analisi delle Componenti Principali	Clustering gerarchico
Campo	Apprendimento automatico	Apprendimento automatico
Famiglia	Machine learning	Machine learning
Anno di origine	2002	1963
Ideatore	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)	Ward, J. H.
Tipo	Unsupervised dimensionality reduction	Unsupervised clustering (agglomerative)
Fonte seminale	Jolliffe, I.T. (2002). Principal Component Analysis (2nd ed.). Springer.	Ward, J. H. (1963). Hierarchical Grouping to Optimize an Objective Function. Journal of the American Statistical Association, 58(301), 236–244.
Alias	Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform	Hiyerarşik Kümeleme, hiyerarşik kümeleme, agglomerative clustering, hierarchical agglomerative clustering
Correlati	3	4
Sintesi	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.	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.