Publication Date

4-27-2026

Document Type

Article

Publication Title

IEEE Transactions on Fuzzy Systems

Volume

34

Issue

7

DOI

10.1109/TFUZZ.2026.3683998

First Page

2171

Last Page

2182

Abstract

Clustering is a fundamental task in statistics and machine learning aimed at identifying homogeneous groups within data. Among the available approaches, fuzzy clustering (FC) is particularly suited to poorly separated boundaries, as it allows partial memberships and captures uncertainty and overlapping structures. We introduce power fuzzy clustering (PFC), a general framework that unifies several FC methods, including fuzzy K-means and probabilistic distance clustering. By complementing the classical fuzzifier with a power parameter on distances, PFC enhances model flexibility while retaining interpretability. To accommodate clusters with different shapes and scales, we incorporate a volume parameter and alternative distance metrics (e.g., Mahalanobis and Minkowski). We further propose the power fuzzy clusterwise regression model, a generalization of PFC that reduces to it when only intercepts are included. Simulation studies investigate the impact of the power and fuzzification parameters, and a real-world application illustrates the method's practical relevance.

Funding Number

GRINS PE00000018 – CUP: E63C22002120006

Funding Sponsor

European Commission

Keywords

clustering with covariates, Clusterwise regression, fuzzy clustering, fuzzy K-means (FKM), probabilistic distance clustering (PDC)

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.

Department

Mathematics and Statistics

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