From Knowledge Discovery to Implementation: A Business Intelligence Approach Using Neural Network Rule Extraction and Decision Tables

  • Christophe Mues
  • Bart Baesens
  • Rudy Setiono
  • Jan Vanthienen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3782)

Abstract

The advent of knowledge discovery in data (KDD) technology has created new opportunities to analyze huge amounts of data. However, in order for this knowledge to be deployed, it first needs to be validated by the end-users and then implemented and integrated into the existing business and decision support environment. In this paper, we propose a framework for the development of business intelligence (BI) systems which centers on the use of neural network rule extraction and decision tables. Two different types of neural network rule extraction algorithms, viz. Neurolinear and Neurorule, are compared, and subsequent implementation strategies based on decision tables are discussed.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Christophe Mues
    • 1
    • 2
  • Bart Baesens
    • 1
  • Rudy Setiono
    • 3
  • Jan Vanthienen
    • 2
  1. 1.School of ManagementUniversity of SouthamptonSouthamptonUnited Kingdom
  2. 2.Dept. of Applied Economic SciencesK.U.LeuvenLeuvenBelgium
  3. 3.Dept. of Information Systems, Kent RidgeNational University of SingaporeSingaporeRepublic of Singapore

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