So sánh phương pháp

	Phân tích thành phần chính	Phân cụm phân cấp
Lĩnh vực	Học máy	Học máy
Họ	Machine learning	Machine learning
Năm ra đời	2002	1963
Người khởi xướng	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)	Ward, J. H.
Loại	Unsupervised dimensionality reduction	Unsupervised clustering (agglomerative)
Công trình gốc	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.
Tên gọi khác	Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform	Hiyerarşik Kümeleme, hiyerarşik kümeleme, agglomerative clustering, hierarchical agglomerative clustering
Liên quan	3	4
Tóm tắt	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.