Using Latent Variables in Model Based Clustering: An E-Government Application

DOI10.1007/978-3-642-32419-2_1zbMath1407.62392OpenAlexW13347878MaRDI QIDQ4644971

Isabella Morlini

Publication date: 9 January 2019

Published in: Recent Developments in Modeling and Applications in Statistics (Search for Journal in Brave)

Full work available at URL: http://hdl.handle.net/11380/730663

zbMATH Keywords

clusteringlatent variableGaussian mixture modelmodel based clustering

Mathematics Subject Classification ID

Gaussian processes (60G15) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics to actuarial sciences and financial mathematics (62P05)

Uses Software

• Latent GOLD

Cites Work

• Unnamed Item
• K-modes clustering.
• A latent variables approach for clustering mixed binary and continuous variables within a Gaussian mixture model

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