Сравнение методов

	Байесовский кластерный анализ	Иерархическая кластеризация
Область	Статистика	Машинное обучение
Семейство	Latent structure	Machine learning
Год появления	1998–2002	1963
Автор метода	Fraley & Raftery (model-based); Dirichlet process formulations by Ferguson (1973) and Antoniak (1974)	Ward, J. H.
Тип	Probabilistic / model-based clustering	Unsupervised clustering (agglomerative)
Основополагающий источник	Fraley, C. & Raftery, A. E. (2002). Model-based clustering, discriminant analysis, and density estimation. Journal of the American Statistical Association, 97(458), 611–631.	Ward, J. H. (1963). Hierarchical Grouping to Optimize an Objective Function. Journal of the American Statistical Association, 58(301), 236–244.
Другие названия	BCA, Bayesian clustering, probabilistic cluster analysis, Bayesian model-based clustering	Hiyerarşik Kümeleme, hiyerarşik kümeleme, agglomerative clustering, hierarchical agglomerative clustering
Связанные	6	4
Сводка	Bayesian cluster analysis assigns observations to latent groups by combining a probabilistic model of within-cluster data with prior beliefs about cluster parameters and the number of clusters. It yields posterior probabilities of cluster membership and principled uncertainty estimates, making it more transparent than classical distance-based clustering algorithms.	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.