Open Access

ARTICLE

Cluster Overlap as Objective Function

Pasi Fränti^1,*, Claude Cariou², Qinpei Zhao³

1 School of Computing, University of Eastern Finland, Joensuu, 80101, Finland
2 Institut d’Electronique et des Technologies du numéRique, University of Rennes—ENSSAT, Lannion, 22305, France
3 School of Computer Science and Technology, Tongji University, Shanghai, 200092, China

* Corresponding Author: Pasi Fränti. Email:

Computers, Materials & Continua 2025, 85(3), 4687-4704. https://doi.org/10.32604/cmc.2025.066534

Received 10 April 2025; Accepted 26 August 2025; Issue published 23 October 2025

Abstract

K-means uses the sum-of-squared error as the objective function to minimize within-cluster distances. We show that, as a consequence, it also maximizes between-cluster variances. This means that the two measures do not provide complementary information and that using only one is enough. Based on this property, we propose a new objective function called cluster overlap, which is measured intuitively as the proportion of points shared between the clusters. We adopt the new function within k-means and present an algorithm called overlap k-means. It is an alternative way to design a k-means algorithm. A localized variant is also provided by limiting the overlap calculation to the neighboring points.

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Keywords

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