A comparison of the mixture and classification approaches to cluster analysis

DOI10.1080/03610928008827932zbMath0465.62053OpenAlexW2132685565MaRDI QIDQ3917347

No author found.

Publication date: 1980

Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610928008827932

zbMATH Keywords

mixturemaximum likelihood estimatesnormal distributionslinear discriminant functionsimulation comparisonseparate sampling schemesclassification clustering approaches

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Monte Carlo methods (65C05) Probabilistic methods, stochastic differential equations (65C99)

Related Items (4)

Mixture Models in View of Evidential Analysis ⋮ Maximum likelihood estimation of Gaussian mixture models without matrix operations ⋮ Estimation of a Discriminant Function Based on Small Sample Size from a Mixture of Two Gumbel Distributions ⋮ Updating a discriminant function on the basis of unclassified data

Cites Work

• Unnamed Item
• A Note on the Generation of Random Normal Deviates
• Estimation Problems with Data from a Mixture
• 389: Separating Mixtures of Normal Distributions
• Population mixture models and clustering algorithms
• The efficiency of a linear discriminant function based on unclassified initial samples
• Asymptotic behaviour of classification maximum likelihood estimates
• A Case Study of two Clustering Methods based on Maximum Likelihood
• Unresolved Problems in Cluster Analysis
• Estimating the components of a mixture of normal distributions
• Separate sample logistic discrimination

This page was built for publication: A comparison of the mixture and classification approaches to cluster analysis