The utility of clusters and a Hungarian clustering algorithm.

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 wa...

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Autores principales:	Alfred Kume, Stephen G Walker
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/26f2d87c175d4debbe0dcd8fb0cf07ae
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spelling	oai:doaj.org-article:26f2d87c175d4debbe0dcd8fb0cf07ae2021-12-02T20:18:44ZThe utility of clusters and a Hungarian clustering algorithm.1932-620310.1371/journal.pone.0255174https://doaj.org/article/26f2d87c175d4debbe0dcd8fb0cf07ae2021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0255174https://doaj.org/toc/1932-6203Implicit 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.Alfred KumeStephen G WalkerPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 8, p e0255174 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Alfred Kume Stephen G Walker The utility of clusters and a Hungarian clustering algorithm.
description	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.
format	article
author	Alfred Kume Stephen G Walker
author_facet	Alfred Kume Stephen G Walker
author_sort	Alfred Kume
title	The utility of clusters and a Hungarian clustering algorithm.
title_short	The utility of clusters and a Hungarian clustering algorithm.
title_full	The utility of clusters and a Hungarian clustering algorithm.
title_fullStr	The utility of clusters and a Hungarian clustering algorithm.
title_full_unstemmed	The utility of clusters and a Hungarian clustering algorithm.
title_sort	utility of clusters and a hungarian clustering algorithm.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/26f2d87c175d4debbe0dcd8fb0cf07ae
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The utility of clusters and a Hungarian clustering algorithm.

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