IDEAS home Printed from https://ideas.repec.org/a/hin/jijmms/4814716.html
   My bibliography  Save this article

Toward a Theory of Normalizing Function of Interestingness Measure of Binary Association Rules

Author

Listed:
  • Armand Armand
  • André Totohasina
  • Daniel Rajaonasy Feno

Abstract

Regarding the existence of more than sixty interestingness measures proposed in the literature since 1993 till today in the topics of association rules mining and facing the importance these last one, the research on normalization probabilistic quality measures of association rules has already led to many tangible results to consolidate the various existing measures in the literature. This article recommends a simple way to perform this normalization. In the interest of a unified presentation, the article offers also a new concept of normalization function as an effective tool for resolution of the problem of normalization measures that have already their own normalization functions.

Suggested Citation

  • Armand Armand & André Totohasina & Daniel Rajaonasy Feno, 2018. "Toward a Theory of Normalizing Function of Interestingness Measure of Binary Association Rules," International Journal of Mathematics and Mathematical Sciences, Hindawi, vol. 2018, pages 1-8, November.
    • Handle: RePEc:hin:jijmms:4814716
    DOI: 10.1155/2018/4814716
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/IJMMS/2018/4814716.pdf
    Download Restriction: no
    File URL: http://downloads.hindawi.com/journals/IJMMS/2018/4814716.xml
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2018/4814716?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Lin Lin & Mei-Ling Shyu & Shu-Ching Chen, 2012. "Association rule mining with a correlation-based interestingness measure for video semantic concept detection," International Journal of Information and Decision Sciences, Inderscience Enterprises Ltd, vol. 4(2/3), pages 199-216.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.

      More about this item

      Statistics

      Access and download statistics

      Corrections

      All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hin:jijmms:4814716. See general information about how to correct material in RePEc.

      If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

      If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

      If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

      For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.com .

      Please note that corrections may take a couple of weeks to filter through the various RePEc services.

      IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.