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標題Title: Efficient Algorithms for Mining Share-Frequent Itemsets
作者Authors: 李育強,Y.-C. Li..等
上傳單位Department: 資訊工程系
上傳時間Date: 2009-12-1
上傳者Author: 李育強
審核單位Department: 資訊工程系
審核老師Teacher: 李育強
檔案類型Categories: 論文Thesis
關鍵詞Keyword: Data mining, Knowledge discovery, Association rules, Share measure
摘要Abstract: 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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2009_12_9e266da5.pdf 169Kb pdf 448 139
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