Skip to main content
SpringerLink
Log in

Parametric Algorithms for Mining Share Frequent Itemsets

  • Published:
Journal of Intelligent Information Systems Aims and scope Submit manuscript

Abstract

Itemset share, the fraction of some numerical total contributed by items when they occur in itemsets, has been proposed as a measure of the importance of itemsets in association rule mining. The IAB and CAC algorithms are able to find share frequent itemsets that have infrequent subsets. These algorithms perform well, but they do not always find all possible share frequent itemsets. In this paper, we describe the incorporation of a threshold factor into these algorithms. The threshold factor can be used to increase the number of frequent itemsets found at a cost of an increase in the number of infrequent itemsets examined. The modified algorithms are tested on a large commercial database. Their behavior is examined using principles of classifier evaluation from machine learning.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Agrawal, A., Swami, A. (1993). Mining Association Rules between Sets of Items in Large Databases. In Proc. ACM SIGMOD Int. Conf. on the Management of Data (pp. 207-216). Washington, D.C.

  • Agrawal, A., Mannila, H., Srikant, R., Toivonen, H., and Verkamo, A.I. (1996). Fast Discovery of Association Rules. In U.M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy (Eds.), Advances in Knowledge Discovery and Data Mining. Menlo Park, California: AAAI Press.

    Google Scholar 

  • Agrawal, A. and Srikant, R. (1994). Fast Algorithms for Mining Association Rules. In Proc. Twentieth Int. Conf. on Very Large Databases (pp. 487-499). Santiago, Chile: Morgan Kaufmann.

    Google Scholar 

  • Barber, B. and Hamilton, H.J. (2000). Algorithms for Mining Share Frequent Itemsets Containing Infrequent Subsets. In Proc. Fourth European Conf. on Principles and Practices of Knowledge Discovery in Databases (pp. 316-324). Lyon, France: Springer.

    Google Scholar 

  • Barber, B. and Hamilton, H.J. (2001). Extracting Share Frequent Itemsets with Infrequent Subsets, Data Mining and Knowledge Discovery, in press.

  • Carter, C.L., Hamilton, H.J., and Cercone, N. (1997). Share Based Measures for Itemsets. In Proc. First European Conf. on the Principles of Data Mining and Knowledge Discovery (pp. 14-24). Trondheim, Norway: Springer.

    Google Scholar 

  • Hilderman, R.J., Carter, C., Hamilton, H.J., and Cercone, N. (1998). Mining Association Rules from Market Basket Data using Share Measures and Characterized Itemsets, Int. J. of Artif. Intell. Tools, 7(2), 189-220.

    Google Scholar 

  • Kohavi, R. and Provost, F. (1998). Glossary of Terms, Machine Learning, 30(2), 271-274.

    Google Scholar 

  • Kubat, M., Holte, R.C., and Matwin, S. (1998). Machine Learning for the Detection of Oil Spills in Satellite Radar Images, Machine Learning, 30(1), 195-215.

    Google Scholar 

  • Mannila, H., Toivonen, H., and Verkamo, A.I. (1994). Efficient Algorithms for Discovering Association Rules. In Proc. 1994 AAAI Workshop on Knowledge Discovery in Databases (pp. 144-155). Seattle,Washington: AAAI Press.

    Google Scholar 

  • Masand, B. and Piatetsky-Shapiro, G. (1996). A Comparison of Approaches for Maximizing Business Payoff of Prediction Models. In Proc. Second Int. Conf. on Knowledge Discovery and Data Mining (pp. 195-201). Portland, Oregon: AAAI Press.

    Google Scholar 

  • Park, J.S., Yu, P.S., and Chen, M. (1997). Mining Association Rules with Adjustable Accuracy. In Proc. Sixth Int. Conf. on Information and Knowledge Management (pp. 151-160).

  • Provost, F. and Fawcett, T. (1997). Analysis and Visualization of Classifier Performance: Comparison under Imprecise Class and Cost Distribution. In Proc. Third Int. Conf. on Knowledge Discovery and Data Mining (pp. 43-48). Newport Beach, California: AAAI Press.

    Google Scholar 

  • Silverstein, C., Brin, S., and Motwani, R. (1998). Beyond Market Baskets: Generalizing Association Rules to Dependence Rules, Data Mining and Knowledge Discovery, 2(1), 39-68.

    Google Scholar 

  • Swets, J.A. (1988). Measuring the Accuracy of Diagnostic Systems, Science, 240, 1285-1293.

    Google Scholar 

  • Zaki, M.J., Parthasarathy, M., Ogihara, M., and Li, W. (1997). New Algorithms for Fast Discovery of Association Rules. In Proc. Third Int. Conf. on Knowledge Discovery and Data Mining (pp. 283-286). Newport Beach, California: AAAI Press.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, University of Regina, Regina, SK, Canada, S4S 0A2

    Brock Barber & HOWARD J. HAMILTON

Authors
  1. Brock Barber
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. HOWARD J. HAMILTON
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

About this article

Cite this article

Barber, B., HAMILTON, H.J. Parametric Algorithms for Mining Share Frequent Itemsets. Journal of Intelligent Information Systems 16, 277–293 (2001). https://doi.org/10.1023/A:1011276003319

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1023/A:1011276003319

Search

Navigation