The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
ClustOfVar: An R Package for the Clustering of Variables
Open accessAbstract
Clustering of variables is as a way to arrange variables into homogeneous clusters, i.e., groups of variables which are strongly related to each other and thus bring the same information. These approaches can then be useful for dimension reduction and variable selection. Several specific methods have been developed for the clustering of numerical variables. However concerning qualitative variables or mixtures of quantitative and qualitative variables, far fewer methods have been proposed. The R package ClustOfVar was specifically developed for this purpose. The homogeneity criterion of a cluster is defined as the sum of correlation ratios (for qualitative variables) and squared correlations (for quantitative variables) to a synthetic quantitative variable, summarizing ``as good as possible'' the variables in the cluster. This synthetic variable is the first principal component obtained with the PCAMIX method. Two clustering algorithms are proposed to optimize the homogeneity criterion: iterative relocation algorithm and ascendant hierarchical clustering. We also propose a bootstrap approach in order to determine suitable numbers of clusters. We illustrate the methodologies and the associated package on small datasets.
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Cites
R: A Language and Environment for Statistical Computing
R Core Team (2025) · doi:10.32614/r.manuals · Cited by 352,181
Finding groups in data: an introduction to cluster analysis
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Clustering of Variables Around Latent Components
Vigneau, É., & Qannari, E. M. (2003) · Communications in Statistics - Simulation and Computation · doi:10.1081/sac-120023882 · Cited by 197
Diametrical clustering for identifying anti-correlated gene clusters
Dhillon, I. S., Marcotte, E. M., & Roshan, U. (2003) · Bioinformatics · doi:10.1093/bioinformatics/btg209 · Cited by 111
Chavent, M., Kuentz-Simonet, V., & Saracco, J. (2012) · Advances in Data Analysis and Classification · doi:10.1007/s11634-012-0105-3 · Cited by 55
Self-citation
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Locke, F. L., Rossi, J. M., Neelapu, S. S., Jacobson, C. A., Miklos, D. B., Ghobadi, A., Oluwole, O. O., Reagan, P. M., Lekakis, L. J., Lin, Y., et al. (2020) · Blood Advances · doi:10.1182/bloodadvances.2020002394 · Cited by 489
Survey of State-of-the-Art Mixed Data Clustering Algorithms
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“…well as advanced clustering and feature selection approaches, e.g., Random Forests (see, e.g., Shi and Horvath, 2006) and PCAMIX method using iterative relocation algorithm and ascendant hierarchical clustering (Chavent et al., 2012) to reduce multicollinearity and exclude any redundant variables.”
Method
Metadata
- Authors
- Chavent, M., Kuentz-Simonet, V., Liquet, B., & Saracco, J.
- Venue
- Journal of Statistical Software
- Publisher
- Foundation for Open Access Statistic
- Published
- 2012
- Volume
- 50
- Issue
- 13
- ISSN
- 1548-7660 (ISSN-L)
- OpenAlex
- W1853119737
- Cited by
- 273
- References
- 6
- FWCI
- 8.23
- Type
- journal article
- Language
- en
- Open access
- diamond
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