Sammenlign metoder

	Principal Component Analysis	Hierarkisk gruppering
Fagområde	Maskinlæring	Maskinlæring
Familie	Machine learning	Machine learning
Oprindelsesår	2002	1963
Ophavsperson	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)	Ward, J. H.
Type	Unsupervised dimensionality reduction	Unsupervised clustering (agglomerative)
Oprindelig kilde	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.
Aliasser	Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform	Hiyerarşik Kümeleme, hiyerarşik kümeleme, agglomerative clustering, hierarchical agglomerative clustering
Relaterede	3	4
Resumé	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.