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Extension Distance-Driven K-Means: A Novel Clustering Framework for Fan-Shaped Data Distributions

Author

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  • Xingsen Li

    (Research Institute of Extenics and Innovation, Guangdong University of Technology, Guangzhou 510006, China
    These authors contributed equally to this work.)

  • Hanqi Yue

    (Research Institute of Extenics and Innovation, Guangdong University of Technology, Guangzhou 510006, China
    These authors contributed equally to this work.)

  • Yaocong Qin

    (Research Institute of Extenics and Innovation, Guangdong University of Technology, Guangzhou 510006, China
    These authors contributed equally to this work.)

  • Haolan Zhang

    (College of Computer and Data Engineering, NingboTech University, Ningbo 315104, China
    These authors contributed equally to this work.)

Abstract

The K-means algorithm utilizes the Euclidean distance metric to quantify the similarity between data points and clusters, with the fundamental objective of assessing the relationship between points. It is important to note that, during the process of clustering, the relationships between the remaining points in the cluster and the points to be measured are ignored. In consideration of the aforementioned issues, this paper proposes the utilization of extension distance for the purpose of evaluating the relationship between the points to be measured and the cluster classes. Furthermore, it introduces a variant of the K-means algorithm based on the separator distance. Through a series of comparative experiments, the effectiveness of the proposed algorithm for clustering fan-shaped datasets is preliminarily verified.

Suggested Citation

  • Xingsen Li & Hanqi Yue & Yaocong Qin & Haolan Zhang, 2025. "Extension Distance-Driven K-Means: A Novel Clustering Framework for Fan-Shaped Data Distributions," Mathematics, MDPI, vol. 13(15), pages 1-15, August.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:15:p:2525-:d:1718648
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