Сравнение на методи

	Анализ на главните компоненти	Йерархично групиране
Област	Машинно обучение	Машинно обучение
Семейство	Machine learning	Machine learning
Година на възникване	2002	1963
Създател	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)	Ward, J. H.
Тип	Unsupervised dimensionality reduction	Unsupervised clustering (agglomerative)
Основополагащ източник	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.
Други названия	Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform	Hiyerarşik Kümeleme, hiyerarşik kümeleme, agglomerative clustering, hierarchical agglomerative clustering
Свързани	3	4
Резюме	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.