Mathematics, Vol. 12, Pages 508: Advancing Spectral Clustering for Categorical and Mixed-Type Data: Insights and Applications

Mathematics, Vol. 12, Pages 508: Advancing Spectral Clustering for Categorical and Mixed-Type Data: Insights and Applications

Mathematics doi: 10.3390/math12040508

Authors: Cinzia Di Nuzzo

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.