Improving the Quality of Association Rule Mining by Means of Rough Sets

  • Daniel Delic
  • Hans-J. Lenz
  • Mattis Neiling
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 16)


We evaluate the rough set and the association rule method with respect to their performance and the quality of the produced rules. It is shown that despite their different approaches, both methods are based on the same principle and, consequently, must generate identical rules. However, they differ strongly with respect to performance Subsequently an optimized association rule procedure is presented which unifies the advantages of both methods.


Association Rule Minimum Support Association Rule Mining Decision Attribute Rule Derivation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Agrawal, R., Imielinski, T., Swami, A. (1993). Mining Association Rules between Sets of Items in Large Databases. In: Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, Washington D.C., USA. 207216. ACM Press.Google Scholar
  2. 2.
    Agrawal, R. and Srikant, S. (1994). Fast Algorithms for Mining Association Rules in Large Databases. In: VLDB’94, 487–499. Morgan Kaufmann.Google Scholar
  3. 3.
    Delic, D. (2001). Data Mining-Abhängigkeitsanalyse von Attributen mit Assoziationsregeln und Rough Sets. MS thesis. Free University of Berlin, Institute of Applied Computer Science, Berlin, Germany.Google Scholar
  4. 4.
    Jürgens, M. and Lenz, H.-J. (2001). Tree Based Indexes Versus Bitmap Indexes: A Performance Study. International Journal of Cooperative Information Systems, 10, 355–376.CrossRefGoogle Scholar
  5. 5.
    Munakata, T. (1998). Rough Sets. In: Fundamentals of the New Artificial Intelligence, 140–182. New York: Springer-Verlag.Google Scholar
  6. 6.
    Pawlak, Z. (1982). Rough Sets. Int. J. Computer and Information Sci, 11, 341–356.MathSciNetCrossRefMATHGoogle Scholar
  7. 7.
    Skowron, A. and Rauszer, C. (1992). The discernibility matrices and functions in information systems. In R. Slowinski (ed.): Intelligent Decision Support. Handbook of Applications and Advances of Rough Sets Theory, 331–362, Dordrecht: Kluwer.Google Scholar
  8. 8.
    Rauszer, C. (1991). Reducts in information systems. Fundamenta Informaticae, 15, 1–12.MathSciNetMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Daniel Delic
    • 1
  • Hans-J. Lenz
    • 1
  • Mattis Neiling
    • 1
  1. 1.Institute of Applied Computer ScienceFree University of BerlinBerlinGermany

Personalised recommendations