Сравнение методов

	Иерархическая кластеризация	Кластеризация методом k-средних	Анализ главных компонент
Область	Машинное обучение	Машинное обучение	Машинное обучение
Семейство	Machine learning	Machine learning	Machine learning
Год появления	1963	1967 (formalized 1982)	2002
Автор метода	Ward, J. H.	MacQueen, J. B.; Lloyd, S. P.	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)
Тип	Unsupervised clustering (agglomerative)	Partitional clustering	Unsupervised dimensionality reduction
Основополагающий источник	Ward, J. H. (1963). Hierarchical Grouping to Optimize an Objective Function. Journal of the American Statistical Association, 58(301), 236–244.	Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129–137.	Jolliffe, I.T. (2002). Principal Component Analysis (2nd ed.). Springer.
Другие названия	Hiyerarşik Kümeleme, hiyerarşik kümeleme, agglomerative clustering, hierarchical agglomerative clustering	k-means clustering, Lloyd's algorithm, k-means partitioning, hard k-means	Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform
Связанные	4	4	3
Сводка	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.	K-means is a classic unsupervised partitional clustering algorithm that divides a dataset into K non-overlapping groups by iteratively assigning each observation to its nearest centroid and updating centroids as the mean of their assigned points. It is one of the most widely used exploratory tools in machine learning and data analysis.	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.