By Topic

Association Rules Mining Based on an Optimized Probability and Statistics Estimate Model

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Yun Yu ; Wuhan Digital Eng. Inst., Wuhan, China ; Wei Chen ; Chang Li

This paper has analysed the a priori algorithm performance, and has pointed out performance bottleneck question of the a priori algorithm. Currently those algorithms to mine association rules only pay attention to one aspect of efficiency or accuracy respectively. There is a paradox between efficiency and accuracy. In order to resolve to this conflict, a novel algorithm based on probability estimate and least square estimate is proposed to mine the association rules from database with the high correlativity and the high confidence. Probability estimate reduce the times of database scanning so as to increase efficiency; least square estimate is based on rigorous and classical mathematical model so as to enhance accuracy. Furthermore, we deduce a recurrence formula to resolve K-itemsets issue. Experimental results have demonstrated that our algorithm is not only efficient but also keeps the completion of frequent items.

Published in:

Computational Intelligence and Industrial Application, 2008. PACIIA '08. Pacific-Asia Workshop on  (Volume:1 )

Date of Conference:

19-20 Dec. 2008