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Advancing Spectral Clustering for Categorical and Mixed-Type Data: Insights and Applications

Author

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  • Cinzia Di Nuzzo

    (Department of Economics and Business, University of Catania, Corso Italia, 55, 95129 Catania, Italy)

Abstract

This study focuses on adapting spectral clustering, a numeric data-clustering technique, for categorical and mixed-type data. The method enhances spectral clustering for categorical and mixed-type data with novel kernel functions, showing improved accuracy in real-world applications. Despite achieving better clustering for datasets with mixed variables, challenges remain in identifying suitable kernel functions for categorical relationships.

Suggested Citation

  • Cinzia Di Nuzzo, 2024. "Advancing Spectral Clustering for Categorical and Mixed-Type Data: Insights and Applications," Mathematics, MDPI, vol. 12(4), pages 1-16, February.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:4:p:508-:d:1334643
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    References listed on IDEAS

    as
    1. Cinzia Di Nuzzo & Salvatore Ingrassia, 2022. "A mixture model approach to spectral clustering and application to textual data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(5), pages 1071-1097, December.
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