The full text of this article:

Redundant association rules reduction techniques
by Mafruz Zaman Ashrafi, David Taniar, Kate Smith
International Journal of Business Intelligence and Data Mining (IJBIDM), Vol. 2, No. 1, 2007
Abstract: To discover hidden correlations, association rule mining methods use two important constraints known as support and confidence. However, mining methods are often unable to find the best value for these constraints: large number of rules when these thresholds are low; very few rules when these thresholds are high. In addition, regardless of these above thresholds, mining methods produce many rules that have identical meaning or, redundant rules. Indeed such redundant rules seem as a main impediment to efficient utilisation of discovered rules, and should be removed. To achieve this aim, here we present several methods that identify those rules that are redundant and eliminate them.

is only available to individual subscribers or to users at subscribing institutions.

Pay per view: If you are not a Subscriber and you just want to read the full contents of this article, please click here to purchase online access to the full-text of this article. Please allow 3 days + mailing time. Current price for article is Thirty Euros (€30)

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Business Intelligence and Data Mining (IJBIDM) journal, that have been redirected here, please check if you have a registered username/password subscription with Inderscience. If that is the case, please Login:

If you are not yet a Subscriber to International Journal of Business Intelligence and Data Mining (IJBIDM) journal, you can subscribe by following a few simple and quick steps. A subscription will give you complete access to all articles in the current issue, as well as to all articles in the previous three years, where applicable. Click here to subscribe.

Should you experience further difficulties or have any enquiries, please email subs@inderscience.com