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New Optimization Methods in Data Mining

In: Operations Research Proceedings 2008

Author

Listed:
  • 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)

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]

Suggested Citation

  • Süureyya Özöğür-Akyüz & Başak Akteke-Öztürk & Tatiana Tchemisova & Gerhard-Wilhelm Weber, 2009. "New Optimization Methods in Data Mining," Springer Books, in: Bernhard Fleischmann & Karl-Heinz Borgwardt & Robert Klein & Axel Tuma (ed.), Operations Research Proceedings 2008, chapter 85, pages 527-532, Springer.
    • Handle: RePEc:spr:sprchp:978-3-642-00142-0_85
    DOI: 10.1007/978-3-642-00142-0_85
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