An Appropriate Abstraction for an Attribute-Oriented Induction

  • Yoshimitsu Kudoh
  • Makoto Haraguchi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1721)

Abstract

An attribute-oriented induction is a useful data mining method that generalizes databases under an appropriate abstraction hierarchy to extract meaningful knowledge. The hierarchy is well designed so as to exclude meaningless rules from a particular point of view. However, there may exist several ways of generalizing databases according to user’s intention. It is therefore important to provide a multi-layered abstraction hierarchy under which several generalizations are possible and are well controlled. In fact, too-general or too-specific databases are inappropriate for mining algorithms to extract significant rules. From this viewpoint, this paper proposes a generalization method based on an information theoretical measure to select an appropriate abstraction hierarchy. Furthermore, we present a system, called ITA (Information Theoretical Abstraction), based on our method and an attribute-oriented induction. We perform some practical experiments in which ITA discovers meaningful rules from a census database US Census Bureau and discuss the validity of ITA based on the experimental results.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1999

Authors and Affiliations

  • Yoshimitsu Kudoh
    • 1
  • Makoto Haraguchi
    • 1
  1. 1.Division of Electronics and Information EngineeringHokkaido UniversitySapporoJapan

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