A local search approximation algorithm for k-means clustering

In k-means clustering we are given a set of n data points in d-dimensional space R d and an integer  k, and the problem is to determine a set of  k points in  R d , called centers, to minimize the mean squared distance from each data point to its nearest center. No exact polynomial-time algorithms a...

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Vydané v:	Computational geometry : theory and applications Ročník 28; číslo 2; s. 89 - 112
Hlavní autori:	Kanungo, Tapas, Mount, David M., Netanyahu, Nathan S., Piatko, Christine D., Silverman, Ruth, Wu, Angela Y.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier B.V 01.06.2004
Predmet:	Approximation algorithms Clustering Computational geometry k-means Local search k-means Local search Computational geometry Approximation algorithms Clustering
ISSN:	0925-7721
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