A mixture model for the classification of three-way proximity data

DOI10.1016/j.csda.2005.02.007zbMath1445.62139OpenAlexW2079169134MaRDI QIDQ959258

Laura Bocci, Donatella Vicari, Maurizio Vichi

Publication date: 11 December 2008

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

Full work available at URL: https://doi.org/10.1016/j.csda.2005.02.007

zbMATH Keywords

consensus partitionfinite mixture of Gaussian densities

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics to psychology (62P15)

Related Items (5)

ROOTCLUS: searching for ``ROOT clusters in three-way proximity data ⋮ GINDCLUS: generalized INDCLUS with external information ⋮ Editorial: Advances in mixture models ⋮ Editorial: Statistical learning methods including dimensionality reduction ⋮ High-dimensional data clustering

Uses Software

• MAPCLUS
• Indclus

Cites Work

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• The representation of three-way proximity data by single and multiple tree structure models
• Model selection and Akaike's information criterion (AIC): The general theory and its analytical extensions
• Maximum likelihood estimation in multidimensional scaling
• Partitions of partitions
• One-mode classification of a three-way data matrix
• Hierarchical clustering schemes
• Mixtures of (constrained) ultrametric trees
• Maximum likelihood estimation via the ECM algorithm: A general framework
• Some Statistical Approaches to Multidimensional Scaling Data
• Model-Based Gaussian and Non-Gaussian Clustering

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