An Approach to Mining Data with Continuous Decision Values

  • Hung Son Nguyen
  • Marta Łuksza
  • Ewa Mąkosa
  • Henryk Jan Komorowski
Conference paper

DOI: 10.1007/3-540-32392-9_78

Part of the Advances in Soft Computing book series (AINSC, volume 31)
Cite this paper as:
Nguyen H.S., Łuksza M., Mąkosa E., Komorowski H.J. (2005) An Approach to Mining Data with Continuous Decision Values. In: Kłopotek M.A., Wierzchoń S.T., Trojanowski K. (eds) Intelligent Information Processing and Web Mining. Advances in Soft Computing, vol 31. Springer, Berlin, Heidelberg

Abstract

We propose a novel approach to discover useful patterns from ill-defined decision tables with a real value decision and nominal conditional attributes. The proposed solution is based on a two-layered learning algorithm. In the first layer the preference relation between objects is approximated from the data. In the second layer the approximated preference relation is used to create three applications: (1) to learn a ranking order on a collection of combinations, (2) to predict the real decision value, (3) to optimize the process of searching for the combination with maximal decision.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Hung Son Nguyen
    • 1
    • 2
  • Marta Łuksza
    • 1
    • 2
  • Ewa Mąkosa
    • 1
    • 2
  • Henryk Jan Komorowski
    • 1
  1. 1.The Linnaeus Centre for BioinformaticsUppsala UniversityUppsalaSweden
  2. 2.Faculty of Mathematics, Informatics and MechanicsWarsaw UniversityWarsawPoland

Personalised recommendations