Approximation algorithm for spherical \begin{document}$ k $\end{document}-means problem with penalty.

Autor:	Wu, Chenchen, Lv, Wei, Wang, Yujie, Xu, Dachuan
Předmět:	COMBINATORIAL optimization DATA mining SEARCH algorithms APPROXIMATION algorithms MATHEMATICS MACHINE learning
Zdroj:	Journal of Industrial & Management Optimization; Jul2022, Vol. 18 Issue 4, p2277-2287, 11p
Abstrakt:	The [Math Processing Error] k -means problem is a classical combinatorial optimization problem which has lots of applications in many fields such as machine learning, data mining, etc. We consider a variant of [Math Processing Error] k -means problem in the spherical space, that is, spherical [Math Processing Error] k -means problem with penalties. In the problem, it is allowable that some nodes in the spherical space can not be clustered by paying some penalty costs. Based on local search scheme, we propose a [Math Processing Error] (4 (11 + 4 7) + ϵ) -approximation algorithm using singe-swap operation, where [Math Processing Error] ϵ is a positive constant. [ABSTRACT FROM AUTHOR]
Databáze:	Complementary Index
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