Clustering of Variables Around Latent Components

DOI10.1081/SAC-120023882zbMath1100.62582OpenAlexW2052231400MaRDI QIDQ4431295

El Mostafa Qannari, Evelyne Vigneau

Publication date: 19 October 2003

Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1081/sac-120023882

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30)

Related Items (13)

Supervised clustering of variables ⋮ Hierarchical disjoint principal component analysis ⋮ Clustering of variables methods and measurement models for soccer players' performances ⋮ A clustering approach to interpretable principal components ⋮ Hierarchical clustering of continuous variables based on the empirical copula process and permutation linkages ⋮ Comparison of three hypothesis testing approaches for the selection of the appropriate number of clusters of variables ⋮ Clustering and disjoint principal component analysis ⋮ A modified PLS path modeling algorithm handling reflective categorical variables and a new model building strategy ⋮ Model-based methods to identify multiple cluster structures in a data set ⋮ Combined use of association rules mining and clustering methods to find relevant links between binary rare attributes in a large data set ⋮ A von Mises-Fisher mixture model for clustering numerical and categorical variables ⋮ Clustering of Variables Based on Watson Distribution on Hypersphere: A Comparison of Algorithms ⋮ Regression based thresholds in principal loading analysis

Uses Software

• SAS/STAT

Cites Work

• Principal Variables
• An Interpretation of Partial Least Squares
• Hierarchical clustering of variables: a comparison among strategies of analysis
• Simple Principal Components
• VARIABLE SELECTION AND INTERPRETATION OF COVARIANCE PRINCIPAL COMPONENTS

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