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Relevance Feedback for Association Rules by Leveraging Concepts from Information Retrieval

  • Georg Ruß
  • Detlef Nauck
  • Mirko Böttcher
  • Rudolf Kruse

Abstract

The task of detecting those association rules which are interesting within the vast set of discovered ones still is a major research hallenge in data mining. Although several possible solutions have been proposed, they usually require a user to be aware what he knows, to have a rough idea what he is looking for, and to be able to specify this knowledge in advance. In this paper we compare the task of finding the most relevant rules with the task of finding the most relevant documents known from Information Retrieval. We propose a novel and flexible method of relevance feedback for association rules which leverages technologies from Information Retrieval, like document vectors, term frequencies and similarity calculations. By acquiring a user’s preferences our approach builds a repository of what he considers to be (non-)relevant. By calculating and aggregating the similarities of each unexamined rule with the rules in the repository we obtain a relevance score which better reflects the user’s notion of relevance with each feedback provided.

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Copyright information

© Springer-Verlag London Limited 2008

Authors and Affiliations

  • Georg Ruß
    • 1
  • Detlef Nauck
    • 2
  • Mirko Böttcher
    • 3
  • Rudolf Kruse
    • 4
  1. 1.Institute for Knowledge and Language Engineering University of MagdeburgGermany
  2. 2.Intelligent Systems Research Centre BT Group IpswichUnited Kingdom
  3. 3.Intelligent Systems Research Centre BT Group IpswichUnited Kingdom
  4. 4.Institute for Knowledge and Language Engineering University of MagdeburgGermany

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