手法を比較

	ベイズクラスター分析	クラスター分析
分野	統計学	統計学
系統	Latent structure	Latent structure
提唱年	1998–2002	1939–1967
提唱者	Fraley & Raftery (model-based); Dirichlet process formulations by Ferguson (1973) and Antoniak (1974)	Robert C. Tryon (early development); Ward (1963) for hierarchical; MacQueen (1967) for k-means
種類	Probabilistic / model-based clustering	Unsupervised classification / grouping
原典	Fraley, C. & Raftery, A. E. (2002). Model-based clustering, discriminant analysis, and density estimation. Journal of the American Statistical Association, 97(458), 611–631.	Everitt, B. S., Landau, S., Leese, M. & Stahl, D. (2011). Cluster Analysis (5th ed.). Wiley. ISBN: 978-0470749913
別名	BCA, Bayesian clustering, probabilistic cluster analysis, Bayesian model-based clustering	clustering, unsupervised classification, data clustering, numerical taxonomy
関連	6	5
概要	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.	Cluster analysis is a family of unsupervised multivariate techniques that partition a set of objects or observations into internally homogeneous, mutually distinct groups — clusters — based on measured characteristics, without any prior knowledge of group membership. It is widely used in market segmentation, bioinformatics, psychology, and social science to reveal natural groupings in data.