Advertisement

Discovery of association rules over ordinal data: A new and faster algorithm and its application to basket analysis

  • Oliver Büchter
  • Rüdiger Wirth
Papers
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1394)

Abstract

This paper argues that quantitative information like prices, amounts bought, and time can give valuable insights into consumer behavior. While Boolean association rules discard any quantitative information, existing algorithms for quantitative association rules can hardly be used for basket analysis. They either lack performance, are restricted to the two-dimensional case, or make questionable assumptions about the data. We propose a new and faster algorithm Q2 for the discovery of multi-dimensional association rules over ordinal data, which is based on ideas presented in [SA96]. Our new algorithm Q2 does not search for quantitative association rules from the very beginning. Instead Q2 prunes out a lot of candidates by first computing the frequent Boolean itemsets. After that, the frequent quantitative itemsets are found in a single pass over the data.

In addition, a new absolute measure for the interestingness of quantitative association rules is introduced. It is based on the view that quantitative association rules have to be interpreted on the background of their Boolean generalizations.

We experimentally compare the new algorithm against the previous approach, obtaining performance improvements of more than an order of magnitude on supermarket data. A rather astonishing result of this paper is that an additional run through the transactions does pay off when searching for quantitative association rules.

Keywords

association rules basket analysis quantitative association rules ordinal data 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [AIS93]
    Rakesh Agrawal, Tomasz Imielinski, and Arun Swami. Mining association rules between sets of items in large databases. In Proceedings of the 1993 ACM SIGMOD Conference, Washington DC, USA, May 1993, 1993.Google Scholar
  2. [AS94]
    Rakesh Agrawal and Ramakrishnan Srikant. Fast algorithms for mining association rules. In Proceedings of the 20th VLDB Conference, Santiago, Chile, 1994.Google Scholar
  3. [Bel97]
    Bell, Michael: A Data Mining FAQ, 1997, [Http://www.gwhy.com/dmfaq.htm, 23.07.97].Google Scholar
  4. [BMS97]
    Sergey Brin, Rajeev Motwani, and Craig Silverstein. Beyond market baskets: Generalizing association rules to correlations. In 1997 ACM SIGMOD Conference on Management of Data, pages 265–276, 1997.Google Scholar
  5. [FMM96]
    Takeshi Fukuda, Yasuhiko Morimoto, Shinichi Morishita, and Takeshi Tokuyama. Mining optimized association rules for numeric attributes. In Proceddings of the 15th ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems. Montreal, Canada, 1996.Google Scholar
  6. [MY97]
    R.J. Miller and Yang Y. Association rues over interval data. In ACM SIGMOD 1997, Tucson, Arizona, May 1997.Google Scholar
  7. [SA96]
    Ramakrishnan Srikant and Rakesh Agrawal. Mining quantitative association rules in large relational tables. In Proc. of the ACM SIGMOD Conference on Management of Data, Montreal, Canada, June 1996.Google Scholar
  8. [Toi96]
    Hannu Toivonen. Discovery of Frequent Patterns in Large Data Collections. PhD thesis, University of Helsinki, Department of Computer Science, November 1996.Google Scholar
  9. [YFM97]
    Kunikazu Yoda, Takeshi Fukuda, Yasuhiko Morimoto, Shinichi Morishita, and Takeshi Tokuyama. Computing optimized rectilinear regions for association rules. In Proceedings of the 3rd International Conference on KDD and Data Mining, Newport Beach California, August 1997.Google Scholar
  10. [ZPO97]
    M.J. Zaki, S. Parthasarathy, M. Ogihara, and W. Li. New algorithms for fast discovery of association rules. In Proceedings of the 3rd International Conference on KDD and Data Mining, Newport Beach California, August 1997.Google Scholar
  11. [ZLZ97]
    Zhaohui Zhang, Yuchang Lu, and Bo Zhang. An effective partitioning-combining algorithm for discovering quantitative association rules. In PAKDD97, 1997.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Oliver Büchter
    • 1
  • Rüdiger Wirth
    • 1
  1. 1.Daimler-Benz Research & Technology FT3/KLUlmGermany

Personalised recommendations