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Fuzzy C-modes clustering with spatial regularization and noise cluster

Pierpaolo D’Urso et al.

AStA Advances in Statistical Analysis2025https://doi.org/10.1007/s10182-025-00547-0article
ABDC C
Weight
0.37

What the paper says

Abstract 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.

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https://doi.org/https://doi.org/10.1007/s10182-025-00547-0

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@article{pierpaolo2025,
  title        = {{Fuzzy C-modes clustering with spatial regularization and noise cluster}},
  author       = {Pierpaolo D’Urso et al.},
  journal      = {AStA Advances in Statistical Analysis},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1007/s10182-025-00547-0},
}

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Fuzzy C-modes clustering with spatial regularization and noise cluster

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Evidence weight

0.37

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.16 × 0.4 = 0.06
M · momentum0.53 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.