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Nomclust 2.0: an R package for hierarchical clustering of objects characterized by nominal variables

Author

Listed:
  • Zdenek Sulc

    (Prague University of Economics and Business)

  • Jana Cibulkova

    (Prague University of Economics and Business)

  • Hana Rezankova

    (Prague University of Economics and Business)

Abstract

In this paper, we present the second generation of the nomclust R package, which we developed for the hierarchical clustering of data containing nominal variables (nominal data). The package completely covers the hierarchical clustering process, from dissimilarity matrix calculation, over the choice of a clustering method, to the evaluation of the final clusters. Through the whole clustering process, similarity measures, clustering methods, and evaluation criteria developed solely for nominal data are used, which makes this package unique. In the first part of the paper, the theoretical background of the methods used in the package is described. In the second part, the functionality of the package is demonstrated in several examples. The second generation of the package is completely rewritten to be more natural for the workflow of R users. It includes new similarity measures and evaluation criteria. We also added several graphical outputs and support for S3 generic functions. Finally, due to code optimizations, the calculation time of dissimilarity matrix calculation was substantially reduced.

Suggested Citation

  • Zdenek Sulc & Jana Cibulkova & Hana Rezankova, 2022. "Nomclust 2.0: an R package for hierarchical clustering of objects characterized by nominal variables," Computational Statistics, Springer, vol. 37(5), pages 2161-2184, November.
    • Handle: RePEc:spr:compst:v:37:y:2022:i:5:d:10.1007_s00180-022-01209-4
    DOI: 10.1007/s00180-022-01209-4
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    References listed on IDEAS

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    1. Zdeněk Šulc & Hana Řezanková, 2019. "Comparison of Similarity Measures for Categorical Data in Hierarchical Clustering," Journal of Classification, Springer;The Classification Society, vol. 36(1), pages 58-72, April.
    2. Linzer, Drew A. & Lewis, Jeffrey B., 2011. "poLCA: An R Package for Polytomous Variable Latent Class Analysis," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 42(i10).
    3. Robert Thorndike, 1953. "Who belongs in the family?," Psychometrika, Springer;The Psychometric Society, vol. 18(4), pages 267-276, December.
    4. Isabella Morlini & Sergio Zani, 2012. "A New Class of Weighted Similarity Indices Using Polytomous Variables," Journal of Classification, Springer;The Classification Society, vol. 29(2), pages 199-226, July.
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