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...
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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 *** show GRSET algorithm to be practicable and efficient.
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