Efficient Algorithms for Mining Share-frequent Itemsets
نویسندگان
چکیده
Itemset share has been proposed to evaluate the significance of itemsets for mining association rules in databases. The Fast Share Measure (FSM) algorithm is one of the best algorithms to discover all share-frequent itemsets efficiently. However, FSM is fast only when dealing with small datasets. In this study, we shall propose a revised version of FSM, called the Enhanced FSM (EFSM) algorithm that speeds up the share-frequent itemsets discovery process. In addition, we shall also present two additional algorithms, SuFSM and ShFSM, developed from EFSM. SuFSM and ShFSM prune the candidates more efficiently than FSM and therefore can improve the performance significantly. Simulation results reveal that the proposed methods perform significantly better than ZSP and FSM, and the performance of ShFSM is the best.
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