A latent variables approach for clustering mixed binary and continuous variables within a Gaussian mixture model

DOI10.1007/s11634-011-0101-zzbMath1284.62384OpenAlexW2066887268MaRDI QIDQ1761311

Isabella Morlini

Publication date: 15 November 2012

Published in: Advances in Data Analysis and Classification. ADAC (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11634-011-0101-z

zbMATH Keywords

clusteringlatent variablesE-governmentinformation and communication technologiesmixed mode datascores estimate

Mathematics Subject Classification ID

Applications of statistics to economics (62P20) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Clustering in the social and behavioral sciences (91C20)

Related Items (9)

Mixtures of general location model with factor analyzer covariance structure for clustering mixed type data ⋮ Mixture models for mixed-type data through a composite likelihood approach ⋮ Model based clustering for mixed data: clustMD ⋮ Constrained clustering with a complex cluster structure ⋮ Distance Metrics and Clustering Methods for Mixed‐type Data ⋮ Model-based clustering of Gaussian copulas for mixed data ⋮ An Overview on the URV Model-Based Approach to Cluster Mixed-Type Data ⋮ Using Latent Variables in Model Based Clustering: An E-Government Application ⋮ A model selection criterion for model-based clustering of annotated gene expression data

Uses Software

• Matlab
• MULTIMIX
• PRELIS
• Latent GOLD

Cites Work

• Unnamed Item
• Unnamed Item
• Analysis of residuals for the multinomial item response model
• Maximum likelihood estimation of the polychoric correlation coefficient
• Modification indices for the 2-PL and the nominal response model
• Sample Selection Bias as a Specification Error
• Mixture model clustering using the MULTIMIX program
• Model-Based Gaussian and Non-Gaussian Clustering
• Identification of Binary Response Models

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