By Topic

GRG: an efficient method for association rules mining on frequent closed itemsets

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)
Li Li ; Sch. of Comput. & Commun. Eng., Southwest Jiaotong Univ., Chengdu, China ; Donghai Zhai ; Fan Jin

In this paper, we propose a graph based algorithm GRG (Graph based method for association Rules Generation) for association rules mining using the frequent closed itemsets groundwork. Association rules mining often base on frequent itemsets which often generates a large number of redundant itemsets that reduce the efficiency. Frequent closed itemsets are subset of frequent itemsets, but they contain all information of frequent itemsets. The most existing methods of frequent closed itemsets mining are apriori-based. The efficiency of those methods is limited to the repeated database scan and the candidate set generation. The new algorithm constructs an association graph to represent the frequent relationship between items, and recursively generates frequent closed itemsets based on that graph. It also constructs a lattice graph of frequent closed itemsets and generates approximate association rules base on lattice graph. It scans the database for only two times, and avoids candidate set generation. GRG shows good performance both in speed and scale up properties.

Published in:

Intelligent Control. 2003 IEEE International Symposium on

Date of Conference:

8-8 Oct. 2003