Clustering Discrete Data Through the Multinomial Mixture Model

DOI10.1080/03610920802162623zbMath1292.60023OpenAlexW2141488761MaRDI QIDQ5495067

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Publication date: 30 July 2014

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

Full work available at URL: https://doi.org/10.1080/03610920802162623

zbMATH Keywords

EM algorithmmaximum likelihoodmultinomial mixtures

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Probability distributions: general theory (60E05) Applications of statistics to psychology (62P15)

Related Items (1)

Forming Clusters from Census Areas with Similar Tabular Statistics

Cites Work

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• On the convergence properties of the EM algorithm
• Note on the consistency of the maximum likelihood estimate for nonidentifiable distributions
• Efficiency versus robustness: The case for minimum Hellinger distance and related methods
• Statistical analysis of finite mixture distributions
• Using Multinomial Mixture Models to Cluster Internet Traffic

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