An Algorithm for Mining Complement-Alternative Relationship Based on Frequent Itemsets
CHAI Yu-Mei1, WANG Chun-Li2,WANG Li-Ming1
School of Information Engineering,Zhengzhou University,Zhengzhou 450001 Department of Computer Science and Engineering,Henan University of Urban Construction,Pingdingshan 467036
Abstract Based on the TOP-k-Closed Miner algorithm, an algorithm for mining frequent itemsets based on index (Index-FIM) is proposed. Bit-vector is used to represent dataset, the breadth expansion pruning strategy and region-index skimming strategy are simultaneously introduced. The experimental results show that Index-FIM has high execution efficiency for mining frequent itemsets in sparse datasets. In order to acquire useful information for direct prediction, another algorithm for complement-alternative relationship mining based on frequent itemsets (CARM) is proposed. The relevant coefficient between the items included in frequent itemsets is computed to get their complement-alternative relationship, and the complementary-alternative relationship is represented by complement-alternative relationship graph (CAG), which is convenient for the decision maker to make reasonable and accurate judgment. Experimental results show that CAG is more efficient and precise than frequent itemsets in expressing information.
CHAI Yu-Mei,WANG Chun-Li,WANG Li-Ming. An Algorithm for Mining Complement-Alternative Relationship Based on Frequent Itemsets[J]. , 2012, 25(1): 157-165.
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2012, Vol. 25 
