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

Cinzia Di Nuzzo ()
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Cinzia Di Nuzzo: Department of Economics and Business, University of Catania, Corso Italia, 55, 95129 Catania, Italy

Mathematics, 2024, vol. 12, issue 4, 1-16

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.

Keywords: spectral clustering; categorical data; mixed-type data; kernel functions (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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