New Optimization Methods in Data Mining

Süureyya Özöğür-Akyüz (), Başak Akteke-Öztürk (), Tatiana Tchemisova () and Gerhard-Wilhelm Weber ()
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Süureyya Özöğür-Akyüz: Institute of Applied Mathematics
Başak Akteke-Öztürk: Institute of Applied Mathematics
Tatiana Tchemisova: University of Aveiro, Department of Mathematics
Gerhard-Wilhelm Weber: Institute of Applied Mathematics

Chapter 85 in Operations Research Proceedings 2008, 2009, pp 527-532 from Springer

Abstract: Summary Generally speaking, an optimization problem consists in maximization or minimization of some function (objective function) f : S → R. The feasible set S ⊆ Rn can be either finite or infinite, and can be described with the help of a finite or infinite number of equalities and inequalities or in the form of some topological structure in Rn. The methods for solution of a certain optimization problem depend mainly on the properties of the objective function and the feasible set. In this paper, we discuss how specific optimization methods of optimization can be used in some specific areas of data mining, namely, in classification and clustering that are considered interrelated [11]

Keywords: Minimal Span Tree; Cluster Problem; Global Optimization Problem; Nonsmooth Optimization; Multiple Kernel Learning (search for similar items in EconPapers)
Date: 2009
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DOI: 10.1007/978-3-642-00142-0_85

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