European Conference on Principles of Data Mining and Knowledge Discovery

PKDD 2000: Principles of Data Mining and Knowledge Discovery pp 148-158

Relative Unsupervised Discretization for Association Rule Mining

  • Marcus-Christopher Lud
  • Gerhard Widmer
Conference paper

DOI: 10.1007/3-540-45372-5_15

Volume 1910 of the book series Lecture Notes in Computer Science (LNCS)
Cite this paper as:
Lud MC., Widmer G. (2000) Relative Unsupervised Discretization for Association Rule Mining. In: Zighed D.A., Komorowski J., Żytkow J. (eds) Principles of Data Mining and Knowledge Discovery. PKDD 2000. Lecture Notes in Computer Science, vol 1910. Springer, Berlin, Heidelberg

Abstract

The paper describes a context-sensitive discretization algorithm that can be used to completely discretize a numeric or mixed numeric-categorical dataset. The algorithm combines aspects of unsupervised (class-blind) and supervised methods. It was designed with a view to the problem of finding association rules or functional dependencies in complex, partly numerical data. The paper describes the algorithm and presents systematic experiments with a synthetic data set that contains a number of rather complex associations. Experiments with varying degrees of noise and “fuzziness” demonstrate the robustness of the method. An application to a large real-world dataset produced interesting preliminary results, which are currently the topic of specialized investigations.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Marcus-Christopher Lud
    • 1
  • Gerhard Widmer
    • 1
    • 2
  1. 1.Austrian Research Institute for Artificial IntelligenceVienna
  2. 2.Department of Medical Cybernetics and Artificial IntelligenceUniversity of ViennaAustria