Compară metode

	Clustering ierarhic bayesian (BHC)	Clustering Ierarhic
Domeniu	Statistică	Învățare automată
Familie	Latent structure	Machine learning
Anul apariției	2005	1963
Autorul original	Katherine Heller & Zoubin Ghahramani	Ward, J. H.
Tip	Probabilistic clustering / model-based hierarchical agglomeration	Unsupervised clustering (agglomerative)
Sursa seminală	Heller, K. A. & Ghahramani, Z. (2005). Bayesian hierarchical clustering. In Proceedings of the 22nd International Conference on Machine Learning (ICML 2005), pp. 297–304. ACM.	Ward, J. H. (1963). Hierarchical Grouping to Optimize an Objective Function. Journal of the American Statistical Association, 58(301), 236–244.
Denumiri alternative	BHC, probabilistic hierarchical clustering, Bayesian agglomerative clustering	Hiyerarşik Kümeleme, hiyerarşik kümeleme, agglomerative clustering, hierarchical agglomerative clustering
Înrudite	6	4
Rezumat	Bayesian hierarchical clustering is a probabilistic agglomerative algorithm that builds a tree of nested cluster merges using Bayesian model comparison at each step. Rather than minimising a geometric linkage criterion, it evaluates at every candidate merge whether the data from two clusters are better explained by a single combined model or by two separate models, yielding a statistically principled dendrogram.	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.