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

Xingsen Li, Hanqi Yue, Yaocong Qin () and Haolan Zhang ()
Additional contact information
Xingsen Li: Research Institute of Extenics and Innovation, Guangdong University of Technology, Guangzhou 510006, China
Hanqi Yue: Research Institute of Extenics and Innovation, Guangdong University of Technology, Guangzhou 510006, China
Yaocong Qin: Research Institute of Extenics and Innovation, Guangdong University of Technology, Guangzhou 510006, China
Haolan Zhang: College of Computer and Data Engineering, NingboTech University, Ningbo 315104, China

Mathematics, 2025, vol. 13, issue 15, 1-15

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.

Keywords: clustering; extenics; extension distance (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/13/15/2525/pdf (application/pdf)
https://www.mdpi.com/2227-7390/13/15/2525/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:13:y:2025:i:15:p:2525-:d:1718648

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().