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The utility of clusters and a Hungarian clustering algorithm

Kume, Alfred, Walker, Stephen G. (2021) The utility of clusters and a Hungarian clustering algorithm. PLoS ONE, 16 (8). Article Number e0255174. ISSN 1932-6203. (doi:10.1371/journal.pone.0255174) (KAR id:98319)


Implicit in the k–means algorithm is a way to assign a value, or utility, to a cluster of points. It works by taking the centroid of the points and the value of the cluster is the sum of distances from the centroid to each point in the cluster. The aim in this paper is to introduce an alternative way to assign a value to a cluster. Motivation is provided. Moreover, whereas the k–means algorithm does not have a natural way to determine k if it is unknown, we can use our method of evaluating a cluster to find good clusters in a sequential manner. The idea uses optimizations over permutations and clusters are set by the cyclic groups; generated by the Hungarian algorithm.

Item Type: Article
DOI/Identification number: 10.1371/journal.pone.0255174
Subjects: Q Science > QA Mathematics (inc Computing science)
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Mathematics, Statistics and Actuarial Science
Funders: University of Kent (
Depositing User: Alfred Kume
Date Deposited: 25 Nov 2022 10:32 UTC
Last Modified: 28 Nov 2022 11:14 UTC
Resource URI: (The current URI for this page, for reference purposes)

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