Сравнение методов

	Кластеризация методом k-средних	Иерархическая кластеризация
Область	Машинное обучение	Машинное обучение
Семейство	Machine learning	Machine learning
Год появления	1967	1963
Автор метода	MacQueen, J.	Ward, J. H.
Тип	Partitional clustering (centroid-based)	Unsupervised clustering (agglomerative)
Основополагающий источник	MacQueen, J. (1967). Some Methods for Classification and Analysis of Multivariate Observations. Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, 1, 281–297.	Ward, J. H. (1963). Hierarchical Grouping to Optimize an Objective Function. Journal of the American Statistical Association, 58(301), 236–244.
Другие названия	K-Ortalamalar Kümeleme, k-ortalamalar kümeleme, k-means, centroid clustering	Hiyerarşik Kümeleme, hiyerarşik kümeleme, agglomerative clustering, hierarchical agglomerative clustering
Связанные	3	4
Сводка	K-Means Clustering is a centroid-based partitional clustering algorithm, traced to J. MacQueen in 1967, that splits data into k clusters by assigning each observation to its nearest cluster centre. It is widely used for marketing segmentation, customer grouping, and exploratory analysis.	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.