Model-based methods to identify multiple cluster structures in a data set

DOI10.1016/j.csda.2007.02.019zbMath1452.62442OpenAlexW2050476729MaRDI QIDQ1020837

Gabriele Soffritti, Giuliano Galimberti

Publication date: 2 June 2009

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

Full work available at URL: https://doi.org/10.1016/j.csda.2007.02.019

zbMATH Keywords

cluster analysismixture modelcluster structureclustering variablesmodel-selection

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Classification and discrimination; cluster analysis (statistical aspects) (62H30)

Related Items (10)

Multivariate cluster-weighted models based on seemingly unrelated linear regression ⋮ Block-Wise Variable Selection for Clustering Via Latent States of Mixture Models ⋮ Modelling the role of variables in model-based cluster analysis ⋮ Multivariate linear regression with non-normal errors: a solution based on mixture models ⋮ A tractable multi-partitions clustering ⋮ Greedy learning of latent tree models for multidimensional clustering ⋮ Using conditional independence for parsimonious model-based Gaussian clustering ⋮ Finite mixture models for clustering multilevel data with multiple cluster structures ⋮ Editorial: Statistical learning methods including dimensionality reduction ⋮ Bayesian bi-clustering methods with applications in computational biology

Uses Software

• mclust
• SAS
• SAS/STAT
• COSA

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Graph-theoretic measures of multivariate association and prediction
• Agglomerative hierarchical clustering of continuous variables based on mutual information
• Model selection and Akaike's information criterion (AIC): The general theory and its analytical extensions
• Cluster analysis. A survey
• Estimating the dimension of a model
• Enhanced model-based clustering, density estimation, and discriminant analysis software:\newline MCLUST
• Principal component analysis.
• Clustering of Variables Around Latent Components
• Identifying Multiple Cluster Structures in a Data Matrix
• Model-Based Clustering, Discriminant Analysis, and Density Estimation
• Hierarchical clustering of variables: a comparison among strategies of analysis
• Clustering Objects on Subsets of Attributes (with Discussion)
• A Generalized Clustering Problem, with Application to DNA Microarrays

This page was built for publication: Model-based methods to identify multiple cluster structures in a data set