Сравнение методов

	Анализ главных компонент	Иерархическая кластеризация
Область	Машинное обучение	Машинное обучение
Семейство	Machine learning	Machine learning
Год появления	2002	1963
Автор метода	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)	Ward, J. H.
Тип	Unsupervised dimensionality reduction	Unsupervised clustering (agglomerative)
Основополагающий источник	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.
Другие названия	Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform	Hiyerarşik Kümeleme, hiyerarşik kümeleme, agglomerative clustering, hierarchical agglomerative clustering
Связанные	3	4
Сводка	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.