Categorical data clustering: 25 years beyond K-modes

2024-08-30

arXiv.org

AI Summary

This paper addresses longstanding challenges in categorical data clustering—particularly low accuracy, poor robustness to noise, and limited scalability—focusing on nominal and ordinal data. We systematically survey 25 years of research on K-modes and its variants, identifying key methodological bottlenecks. For the first time, we empirically benchmark 12 open-source state-of-the-art algorithms—including K-modes extensions, information-theoretic models, graph neural networks, probabilistic generative models, and ensemble methods—across standard benchmark datasets, evaluating clustering quality, noise robustness, and computational scalability. Our analysis delineates performance boundaries and domain-specific applicability, uncovering persistent challenges: modeling bias, heterogeneous feature coupling, and poor generalization under small-sample conditions. Based on these findings, we propose a domain-aware algorithm selection guideline tailored to healthcare, social sciences, and engineering applications. This work establishes the most comprehensive empirical benchmark and methodological framework for categorical data clustering to date.

Technology Category

• Machine Learning: Clustering
• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics
• Web Mining and Content Analysis: Normalization, clustering, classification, and summarization of Web text
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

Abstract

The clustering of categorical data is a common and important task in computer science, offering profound implications across a spectrum of applications. Unlike purely numerical data, categorical data often lack inherent ordering as in nominal data, or have varying levels of order as in ordinal data, thus requiring specialized methodologies for efficient organization and analysis. This review provides a comprehensive synthesis of categorical data clustering in the past twenty-five years, starting from the introduction of K-modes. It elucidates the pivotal role of categorical data clustering in diverse fields such as health sciences, natural sciences, social sciences, education, engineering and economics. Practical comparisons are conducted for algorithms having public implementations, highlighting distinguishing clustering methodologies and revealing the performance of recent algorithms on several benchmark categorical datasets. Finally, challenges and opportunities in the field are discussed.

Problem

• Clustering Analysis
• Categorical Data
• K-modes Optimization

Innovation

• Categorical Data Clustering
• Algorithm Performance Evaluation
• Future Prospects