@article{Fränti2025, 
author = {Pasi Fränti and Claude Cariou and Qinpei Zhao},
title = {Cluster Overlap as Objective Function},
year = {2025},
journal = {Computers, Materials & Continua},
volume = {85},
number = {3},
pages = {4687-4704},
keywords = {Clustering, k-means, overlap measure, within-cluster distance, between-cluster distance, arbitrary-shape clusters},
url = {https://www.sciopen.com/article/10.32604/cmc.2025.066534},
doi = {10.32604/cmc.2025.066534},
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.}
}