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Mining Association Rules from Relational Data – Average Distance Based Method

  • Vladimír Bartík
  • Jaroslav Zendulka
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2888)

Abstract

The paper describes a new method for association rule discovery in relational databases, which contain both quantitative and categorical attributes. Most of the methods developed in the past are based on initial equi-depth discretization of quantitative attributes. These approaches bring the loss of information. Distance-based methods are another kind of methods. They try to respect the semantics of data. The basic idea of the new method is to separate processing of categorical and quantitative attributes. The first step finds frequent itemsets containing only values of categorical attributes and then quantitative attributes are processed one by one. Discretization of values during quantitative attributes processing is distance-based. A new measure called average distance is introduced for these purposes. The paper describes the method and results of several experiments on real world data.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Vladimír Bartík
    • 1
  • Jaroslav Zendulka
    • 1
  1. 1.Faculty of Information TechnologyBrno University of TechnologyBrnoCzech Republic

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