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Itemset materializing for fast mining of association rules

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Book cover Advances in Databases and Information Systems (ADBIS 1998)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1475))

Abstract

Mining association rules is an important data mining problem. Association rules are usually mined repeatedly in different parts of a database. Current algorithms for mining association rules work in two steps. First, the most frequently occurring sets of items are discovered, then the sets are used to generate the association rules. The first step usually requires repeated passes over the analyzed database and determines the overall performance. In this paper, we present a new method that addresses the issue of discovering the most frequently occurring sets of items. Our method consists in materializing precomputed sets of items discovered in logical database partitions. We show that the materialized sets can be repeatedly used to efficiently generate the most frequently occurring sets of items. Using this approach, required association rules can be mined with only one scan of the database. Our experiments show that the proposed method significantly outperforms the well-known algorithms.

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References

  1. Agrawal R., Imielinski T., Swami A., “Mining Association Rules Between Sets of Items in Large Databases”, Proc. ACM SIGMOD, pp. 207–216, Washington DC, USA, May 1993

    Google Scholar 

  2. Agrawal R., Mehta M., Shafer J., Srikant R., Arning A., Bollinger T., “The Quest Data Mining System”, Proc. of the 2nd Int’l Conference on Knowledge Discovery in Databases and Data Mining, Portland, Oregon, August 1996

    Google Scholar 

  3. Agrawal R., Srikant R., “Fast Algorithms for Mining Association Rules”, Proc. 20th Int’l Conf. Very Large Data Bases, pp. 478–499, Santiago, Chile, 1994

    Google Scholar 

  4. Cheung D.W., Han J., Ng V., Wong C.Y., “Maintenance of Discovered Association Rules in Large Databases: An Incremental Updating Technique”, Proc. Int’l Conf. Dana Eng., New Orleans, USA, February 1996

    Google Scholar 

  5. Fayyad U., Piatetsky-Shapiro G., Smyth P., “The KDD Process for Extracting Useful Knowledge from Volumes of Data”, Communications of the ACM, Vol. 39, No. 11, Nov. 1996

    Google Scholar 

  6. Han J., “Towards On-Line Analytical Mining in Large Databases, SIGMOD Record”, Vol. 27, No. 1, March 1998

    Google Scholar 

  7. Houtsma M., Swami A., “Set-Oriented Mining of Association Rules”, Research Report RJ 9567, IBM Almaden Research Center, San Jose, California, USA, October 1993

    Google Scholar 

  8. Imielinski T., Manilla H., “A Database Perspective on Knowledge Discovery”, Communications of the ACM, Vol. 39, No. 11, Nov. 1996

    Google Scholar 

  9. Manilla H., Toivonen H., Inkeri Verkamo A., “Efficient Algorithms for Discovering Association Rules”, Proc. AAAI Workshop Knowledge Discovery in Databases, pp. 181–192, July 1994

    Google Scholar 

  10. Manilla H., Toivonnen H., “Levelwise Search and Borders of Theories in Knowledge Discovery”, Report C-1997-8, University of Helsinki, Finland

    Google Scholar 

  11. Morzy T., Zakrzewicz M., “SQL-Like Language For Database Mining”, ADBIS’97 Symposium, St. Petersburg, September 1997

    Google Scholar 

  12. Park J.S., Chen M.-S., Yu P. S., “An Effective Hash-Based Algorithm for Mining Association Rules”, SIGMOD’95, San Jose, CA, USA, 1995

    Google Scholar 

  13. Piatetsky-Shapiro G., Frawley W.J., editors, Knowledge Discovery in Databases, MIT Press, 1991

    Google Scholar 

  14. Savasere, E. Omiecinski, S. Navathe, “An Efficient Algorithm for Mining Association Rules in Large Databases”, Proc. 21th Int’l Conf. Very Large Data Bases, pp. 432–444, Zurich, Switzerland, September 1995

    Google Scholar 

  15. Toivonen H., “Sampling Large Databases for Association Rules”, Proc. 22nd Int’l Conf. Very Large Data Bases, Bombay, India, 1996

    Google Scholar 

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Author information

Authors and Affiliations

  1. Institute of Computing Science, Poznan University of Technology, ul. Piotrowo 3a, 60-965, Poznan, Poland

    Marek Wojciechowski & Maciej Zakrzewicz

Authors
  1. Marek Wojciechowski
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  2. Maciej Zakrzewicz
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Witold Litwin Tadeusz Morzy Gottfried Vossen

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© 1998 Springer-Verlag Berlin Heidelberg

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Wojciechowski, M., Zakrzewicz, M. (1998). Itemset materializing for fast mining of association rules. In: Litwin, W., Morzy, T., Vossen, G. (eds) Advances in Databases and Information Systems. ADBIS 1998. Lecture Notes in Computer Science, vol 1475. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0057741

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  • DOI: https://doi.org/10.1007/BFb0057741

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-64924-3

  • Online ISBN: 978-3-540-68309-4

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