skip to main content
10.1145/1830483.1830562acmconferencesArticle/Chapter ViewAbstractPublication PagesgeccoConference Proceedingsconference-collections
poster

Pruning association rules using statistics and genetic relation algoritm

Published: 07 July 2010 Publication History

Abstract

Most of the classification methods proposed produces too many rules for humans to read over, that is, the number of generated rules is thousands or millions which means complex and hardly understandable for the users.
In this paper, a new post-processing pruning method for class association rules is proposed by a combination of statistics and an evolutionary method named Genetic Relation Algorithm (GRA). The algorithm is carried out in two phases. In the first phase the rules are pruned depending on their matching degree and in the second phase GRA selects the most interesting rules using the distance between them and their strength.

References

[1]
C. Zhang and S. Zhang, Association Rule Mining: models and algorithms, Springer, 2002.
[2]
K. Shimada, K. Hirasawa and J. Hu, "Class Association Rule Mining with Chi-Squared Test Using Genetic Network Programming", In Proc. of the IEEE Conference on Systems, Man and Cybernetics, pp. 5338--5344, Taipei, 2006/10.
[3]
K. Shimada, K. Hirasawa and T. Furuzuki, "Genetic Network Programming with Acquisition Mechanisms of Association Rules", Journal of Advanced Computational Intelligence and Intelligent Informatics, Vol. 10, No. 1, pp. 102--111, 2006.
[4]
C. Blake and C. Merz, UCI Repository of machine learning databases, http://www.ics.uci.edu/mlearn/MLRepository.html.

Index Terms

  1. Pruning association rules using statistics and genetic relation algoritm

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Conferences
    GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computation
    July 2010
    1520 pages
    ISBN:9781450300728
    DOI:10.1145/1830483

    Sponsors

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 07 July 2010

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tags

    1. association rule mining
    2. evolutionary computation
    3. genetic relation algorithm
    4. pruning

    Qualifiers

    • Poster

    Conference

    GECCO '10
    Sponsor:

    Acceptance Rates

    Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • 0
      Total Citations
    • 136
      Total Downloads
    • Downloads (Last 12 months)1
    • Downloads (Last 6 weeks)0
    Reflects downloads up to 20 Jan 2025

    Other Metrics

    Citations

    View Options

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Media

    Figures

    Other

    Tables

    Share

    Share

    Share this Publication link

    Share on social media