Representative association rules

  • Marzena Kryszkiewicz
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1394)


Discovering association rules between items in a large database is an important database mining problem. The number of association rules may be huge. In this paper, we define a cover operator that logically derives a set of association rules from a given association rule. Representative association rules are defined as a least set of rules that covers all association rules satisfying certain user specified constraints. A user may be provided with a set of representative association rules instead of the whole set of association rules. The association rules, which are not representative ones, may be generated on demand by means of the cover operator. In this paper, we offer an algorithm computing representative association rules.


representative association rules cover operator k-rule 


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  1. 1.
    Agraval R., Imielinski T., Swami A., Mining Associations Rules between Sets of Items in Large Databases. In Proc. of the ACM SIGMOD Conference on Management of Data, Washington, D.C., May 1993, pp. 207–216.Google Scholar
  2. 2.
    Srikant R., Agraval R., Mining Generalized Association Rules. In Proc. of the 21st VLDB Conference, Zurich, Swizerland, 1995, pp. 407–419.Google Scholar
  3. 3.
    Imielinski T., Virmani A., Abdulghani A., Discover Board Application Programming Interface and Query Language for Database Mining. In Proc. of KDD '96, Portland Ore., August 1996, pp. 20–26.Google Scholar
  4. 4.
    Meo R., Psaila G., Ceri S., A New SQL-like Operator for Mining Asscociation Rules, Proc. of the 22nd VLDB Conference, Mumbai (Bombay), India, 1996.Google Scholar
  5. 5.
    Communications of the ACM, November 1996-Vol. 39, No 11.Google Scholar
  6. 6.
    Advances in Knowledge Discovery and Data Mining, eds. Menlo Park, California, 1996.Google Scholar
  7. 7.
    Piatetsky-Shapiro G., Discovery, Analysis and Presentation of Strong Rules. In Knowledge Discovery in Databases, G. Piatetsky-Shapiro, W. Frawley, eds., AAAI/MIT Press, Menlo Park, CA, 1991, pp. 229–248.Google Scholar
  8. 8.
    Agraval R., Mannila H., Srikant R., Toivonen H., Verkamo A.I., Fast Discovery of Association Rules. In Advances in Knowledge Discovery and Data Mining, U.M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, R. Uthurusamy, eds., AAAI, Menlo Park, California, 1996, pp. 307–328.Google Scholar
  9. 9.
    Savasere A, Omiecinski E., Navathe S., An Efficient Algorithm for Mining Association Rules in Large Databases. In Proc. of the 21st VLDB Conference, Zurich, Swizerland, 1995, pp. 432–444.Google Scholar
  10. 10.
    Houtsma M., Swami A., Set-oriented Mining of Association Rules. In Int'l Conference on Data Engineering, Taipei, Taiwan, March 1995.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Marzena Kryszkiewicz
    • 1
  1. 1.Institute of Computer ScienceWarsaw University of TechnologyWarsawPoland

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