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

被引：2

作者：

Di Nuzzo, Cinzia ^{[1
]}

机构：

[1] Univ Catania, Dept Econ & Business, Corso Italia 55, I-95129 Catania, Italy

来源：

MATHEMATICS | 2024年 / 12卷 / 04期

关键词：

spectral clustering; categorical data; mixed-type data; kernel functions; 6207; 6209;

D O I：

10.3390/math12040508

中图分类号：

O1 [数学];

学科分类号：

0701 ; 070101 ;

摘要：

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.

引用

页数：16

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