Comparison of the mixture and the classification maximum likelihood in cluster analysis with binary data

DOI10.1016/S0167-9473(96)00021-7zbMath0900.62325OpenAlexW2013839947MaRDI QIDQ1350802

Gérard Govaert, Mohamed Nadif

Publication date: 27 February 1997

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/s0167-9473(96)00021-7

zbMATH Keywords

Maximum likelihoodClassification maximum likelihoodEqual or unknown, mixing proportionsSimulation comparison

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30)

Related Items (7)

Speed-up for the expectation-maximization algorithm for clustering categorical data ⋮ Fitting mixtures of linear regressions ⋮ Clustering of contingency table and mixture model ⋮ Modeling heterogeneous peer assortment effects using finite mixture exponential random graph models ⋮ On multivariate binary data clustering and feature weighting ⋮ Latent Block Model for Contingency Table ⋮ Block clustering with Bernoulli mixture models: comparison of different approaches

Cites Work

• Unnamed Item
• Unnamed Item
• Clustering criteria for discrete data and latent class models
• A classification EM algorithm for clustering and two stochastic versions
• Classification and Mixture Approaches to Clustering via Maximum Likelihood
• Clustering Criteria and Multivariate Normal Mixtures
• Estimating the components of a mixture of normal distributions

This page was built for publication: Comparison of the mixture and the classification maximum likelihood in cluster analysis with binary data