Work

Fuzzy C-modes clustering with spatial regularization and noise cluster

Year: 2025

Type: article

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.

Source: AStA Advances in Statistical Analysis

DOI: 10.1007/s10182-025-00547-0

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

Institutions: Sapienza University of Rome, Libera Università Internazionale degli Studi Sociali Guido Carli

Language: English

Topic: Advanced Clustering Algorithms Research

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Sustainable cities and communities

Open Access status: hybrid

Estimated APC paid: $2,990