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

The classical algorithm of finding association rules generated by a frequent itemset has to generate all nonempty 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.

作 者： WU Kun JIANG Bao-qing WEI Qing   作者單位： WU Kun(Institute of Data and Knowledge Engineering, Henan University, Kaifeng 475001;The Personnel Department, Zhengzhou Institute of Aeronautical Industry Management,Zhengzhou 450000)

JIANG Bao-qing(Institute of Data and Knowledge Engineering, Henan University, Kaifeng 475001)

WEI Qing(The Computer Science Department, Henan University of Finance and Economics, Zhengzhou 450000) 

刊 名： 東華大學(xué)學(xué)報(bào)（英文版）  EI 英文刊名： JOURNAL OF DONGHUA UNIVERSITY(ENGLISH EDITION)  年，卷(期)： 2006 23(6)  分類號(hào)： O1  關(guān)鍵詞： association rule   frequent itemset   breath-first   depth-first   consequent  

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