Finite mixture models for clustering multilevel data with multiple cluster structures

DOI10.1177/1471082X0801000302MaRDI QIDQ4970584

Giuliano Galimberti, Gabriele Soffritti

Publication date: 7 October 2020

Published in: Statistical Modelling (Search for Journal in Brave)

zbMATH Keywords

model selectioncluster analysismixture modelcluster structuremultilevel data

Mathematics Subject Classification ID

Statistics (62-XX)

Uses Software

• mclust
• COSA

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Model-based methods to identify multiple cluster structures in a data set
• Synthesized clustering: A method for amalgamating alternative clustering bases with differential weighting of variables
• Model selection and Akaike's information criterion (AIC): The general theory and its analytical extensions
• Estimating the dimension of a model
• Enhanced model-based clustering, density estimation, and discriminant analysis software:\newline MCLUST
• Weighting and selection of variables for cluster analysis
• A variable-selection heuristic for K-means clustering
• Latent class and finite mixture models for multilevel data sets
• Identifying Multiple Cluster Structures in a Data Matrix
• Model-Based Clustering, Discriminant Analysis, and Density Estimation
• Clustering Objects on Subsets of Attributes (with Discussion)
• A Generalized Clustering Problem, with Application to DNA Microarrays
• Variable Selection for Model-Based Clustering

This page was built for publication: Finite mixture models for clustering multilevel data with multiple cluster structures