Work

Fuzzy clustering with three-way semi-perimeter distance

Year: 2026

Type: article

Abstract: In this paper, we propose a novel clustering method based on three-way distances. We define a fuzzy c -medoid algorithm based on the semi-perimeter three-way distance, which can be derived from any classical (two-way) distance. This algorithm minimizes an objective function defined as a linear combination of the three-way distances between the medoid of each cluster and call couples of members of the cluster, and an entropic regularization term. We test its effectiveness, applying it both to simulated data and data from the Italian census, showing how it outperforms the standard fuzzy c -medoids algorithm.

Source: Statistics and Computing

DOI: 10.1007/s11222-026-10961-2

Authors: Pierpaolo D’Urso, Lorenzo Federico

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

Open Access status: hybrid

Estimated APC paid: $2,890

Funder: Libera Università Internazionale degli Studi Sociali Guido Carli