IDEAS home Printed from https://ideas.repec.org/a/ids/injdan/v4y2012i1p101-114.html
   My bibliography  Save this article

Normalised support: a virtual angle of measurement of 'interestingness'

Author

Listed:
  • Waleed Alsabhan

Abstract

Association rule mining is applied to large databases to identify product associations. In the resulting large number of rules, interestingness is difficult to determine. Researchers have defined various measures of 'interestingness' such as support, confidence, lift and gain. Support is the probability of occurrence of an item or set of items, and is the most important of these measures, since the other measures are calculated using support. This current research suggests some deficiencies in the support measure and shows it is not consistent with its definition. Because other measures are calculated using support, this may make the other measures inconsistent. The researcher in this study proposes a new measure called normalised support, which is normalisation of general support, in other context-adjusted support or penalised support. Normalised support recommendations can stabilise product sale by product cross-sell promotion. In addition, the usefulness of other measures improves automatically.

Suggested Citation

  • Waleed Alsabhan, 2012. "Normalised support: a virtual angle of measurement of 'interestingness'," International Journal of Data Analysis Techniques and Strategies, Inderscience Enterprises Ltd, vol. 4(1), pages 101-114.
    • Handle: RePEc:ids:injdan:v:4:y:2012:i:1:p:101-114
    as

    Download full text from publisher

    File URL: http://www.inderscience.com/link.php?id=45124
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. R.B.V. Subramanyam & A. Goswami & Bhanu Prasad, 2008. "Mining fuzzy temporal patterns from process instances with weighted temporal graphs," International Journal of Data Analysis Techniques and Strategies, Inderscience Enterprises Ltd, vol. 1(1), pages 60-77.
    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.

      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:ids:injdan:v:4:y:2012:i:1:p:101-114. 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: Sarah Parker (email available below). General contact details of provider: http://www.inderscience.com/browse/index.php?journalID=282 .

      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.