Salīdzināt metodes

	Bayesiskā klasteru analīze	Klasteru analīze
Nozare	Statistika	Statistika
Saime	Latent structure	Latent structure
Izcelsmes gads	1998–2002	1939–1967
Autors	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
Tips	Probabilistic / model-based clustering	Unsupervised classification / grouping
Pirmavots	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
Citi nosaukumi	BCA, Bayesian clustering, probabilistic cluster analysis, Bayesian model-based clustering	clustering, unsupervised classification, data clustering, numerical taxonomy
Saistītās	6	5
Kopsavilkums	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.