Journal of Intelligent Information Systems

, Volume 20, Issue 3, pp 255–283 | Cite as

A Statistical Theory for Quantitative Association Rules

Article

Abstract

Association rules are a key data-mining tool and as such have been well researched. So far, this research has focused predominantly on databases containing categorical data only. However, many real-world databases contain quantitative attributes and current solutions for this case are so far inadequate. In this paper we introduce a new definition of quantitative association rules based on statistical inference theory. Our definition reflects the intuition that the goal of association rules is to find extraordinary and therefore interesting phenomena in databases. We also introduce the concept of sub-rules which can be applied to any type of association rule. Rigorous experimental evaluation on real-world datasets is presented, demonstrating the usefulness and characteristics of rules mined according to our definition.

data mining knowledge discovery in data bases quantitative association rules statistical inference theory 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Agrawal, R., Imielinski, T., and Swami, A. (1993). Mining Association Rules between Sets of Items in Large Databases. In Proc. of the 1993 ACM SIGMOD Intl. Conference on Management of Data (pp. 207–216).Google Scholar
  2. Agrawal, R. and Srikant, R. (1994). Fast Algorithms for Mining Association Rules in Large Databases. In Proc. of the 20th Intl. Conference on VLDB.Google Scholar
  3. Brin, S., Motwani, R., and Silverstein, C. (1997). Beyond Market Baskets: Generalizing Association Rules to Correlations. In Proc. of the 1997 ACM SIGMOD Conference on Management of Data.Google Scholar
  4. Fukuda, T., Morimoto, Y., Morishita, S., and Tokuyama, T. (1996). Data Mining Using Two-Dimensional Optimized Association Rules: Scheme, Algorithms and Visualization. In Proc. of the 1996 ACM SIGMOD Conference on Management of Data.Google Scholar
  5. Fukuda, T., Morimoto, Y., Morishita, S., and Tokuyama, T. (1999). Mining Optimized Association Rules for Numeric Attributes. Journal of Computer and System Sciences, 58, 1–12.Google Scholar
  6. Jensen, D. and Cohen, P. (2000). Multiple Comparisons in Induction Algorithms. Journal of Machine Learning, 38, 309–338.Google Scholar
  7. Kloesgen,W. (1994). Exploration of Simulation Experiments by Discovery. In Proceedings of KDD-94Workshop, AAAI-94. Further information may be found at the explora homepage: http://orgwis.gmd.de/projects/explora.Google Scholar
  8. Lindgren, B.W. (1976). Statistical Theory. New York: Macmillan Publishing Co., Inc.Google Scholar
  9. Mannila, H., Toivonen, H., and Verkamo, A.I. (1994). Efficient Algorithms for Discovering Association Rules. KDD-94: AAAI Workshop on Knowledge Discovery in Databases (pp. 181–192).Google Scholar
  10. Morimoto, Y., Ishii, H., and Morishita, S. (2001). Efficient Construction of Regression Trees with Range and Region Splitting. Machine Learning, 45, 235–259.Google Scholar
  11. Piatetsky-Shapiro, G. (1991). Discovery, Analysis, and Presentation of Strong Rules. In Knowledge Discovey in Databases (pp. 229–248).Google Scholar
  12. Srikant, R. and Agrawal, R. (1996). Mining Quantitative Association Rules in Large Relational Tables. In Proc. of the ACM SIGMOD Conference on Management of Data.Google Scholar
  13. Toivonen, H. (1996). Sampling Large Databases for Association Rules. In Proc. of the 22nd VLDB Conference.Google Scholar
  14. Yoda, K., Fukuda, T., Morimoto, Y., Morishita, S., and Tokuyama, T. (1997) Computing Optimized Rectilinear Regions for Association Rules. In Proc. of KDD' 97.Google Scholar
  15. Zhang, Z., Lu, Y., and Zhang, B. (1997). An Effective Partitioning-Combining Algorithm for Discovering Quantitative Association Rules. In Proc. of the First Pacific-Asia Conference on Knowledge Discovery and Data Mining.Google Scholar

Copyright information

© Kluwer Academic Publishers 2003

Authors and Affiliations

  1. 1.Department of Computer ScienceBar-Ilan UniversityRamat GanIsrael
  2. 2.IBM T.J. Watson ResearchHawthorneUSA

Personalised recommendations