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<p>Clustering categorical data presents unique challenges that traditional techniques do not adequately address. This paper proposes an extension of the fuzzy C-modes algorithm. By incorporating a noise cluster and integrating spatial contiguity relationships among units, the algorithm’s robustness is significantly enhanced. Performance evaluations using synthetic data demonstrate the efficacy of the proposed algorithm in handling both global and local outliers. Furthermore, the paper discusses the application of the algorithm to real-world data on sustainable urban mobility in the Italian provincial capitals during 2021, highlighting its practical relevance and potential impact in real-world scenarios.</p>

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Fuzzy C-modes clustering with spatial regularization and noise cluster

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月21日

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  • Pierpaolo D’Urso,
  • Livia De Giovanni,
  • Lorenzo Federico,
  • Vincenzina Vitale

摘要

Clustering categorical data presents unique challenges that traditional techniques do not adequately address. This paper proposes an extension of the fuzzy C-modes algorithm. By incorporating a noise cluster and integrating spatial contiguity relationships among units, the algorithm’s robustness is significantly enhanced. Performance evaluations using synthetic data demonstrate the efficacy of the proposed algorithm in handling both global and local outliers. Furthermore, the paper discusses the application of the algorithm to real-world data on sustainable urban mobility in the Italian provincial capitals during 2021, highlighting its practical relevance and potential impact in real-world scenarios.