A Depth-first Algorithm of Finding All Association Rules Generated by a Frequent Itemset

作     者：武坤 姜保庆 魏庆 

作者机构：Institute of Data and Knowledge EngineeringHenan University Institute of Data and Knowledge Engineering Henan University The Computer Science Department Henan University of Finance and Economics 

出 版 物：《Journal of Donghua University(English Edition)》 (东华大学学报（英文版）)

年 卷 期：2006年第23卷第6期

页      面：1-4,9页

核心收录：

学科分类：07[理学] 0701[理学-数学] 070101[理学-基础数学] 

基　　金：Supported by the National Natural Science Foundation of China (No.60474022) the Natural Science Foundation of Henan Province(No. G2002026,200510475028) 

主　　题：association rule frequent itemset breath-first,depth-first consequent. 

摘      要：The classical algorithm of finding association rules generated by a frequent itemset has to generate all non-empty subsets of the frequent itemset as candidate set of consequents. Xiongfei Li aimed at this and proposed an improved algorithm. The algorithm finds all consequents layer by layer, so it is breadth-first. In this paper, we propose a new algorithm Generate Rules by using Set-Enumeration Tree (GRSET) which uses the structure of Set-Enumeration Tree and depth-first method to find all consequents of the association rules one by one and get all association rules correspond to the consequents. Experiments show GRSET algorithm to be practicable and efficient.