@Article{cmc.2025.066534,
AUTHOR = {Pasi Fränti, Claude Cariou, Qinpei Zhao},
TITLE = {Cluster Overlap as Objective Function},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {85},
YEAR = {2025},
NUMBER = {3},
PAGES = {4687--4704},
URL = {http://www.techscience.com/cmc/v85n3/64138},
ISSN = {1546-2226},
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 <i>cluster overlap</i>, 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 <i>overlap k-means</i>. 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.},
DOI = {10.32604/cmc.2025.066534}
}