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    ASIA unversity > 資訊學院 > 資訊工程學系 > 期刊論文 >  Item 310904400/6425

    Please use this identifier to cite or link to this item: http://asiair.asia.edu.tw/ir/handle/310904400/6425

    Title: Isolated items discarding strategy for discovering high utility itemsets
    Authors: Li, Y. C.;Yeh, J. S.;Chang, C. C.
    Keywords: Data mining;Association rule;Utility mining
    Date: 2008-01
    Issue Date: 2009-12-17 14:57:53 (UTC+8)
    Publisher: Asia University
    Abstract: Traditional methods of association rule mining consider the appearance of an item in a transaction, whether or not it is purchased, as a binary variable. However, customers may purchase more than one of the same item, and the unit cost may vary among items. Utility mining, a generalized form of the share mining model, attempts to overcome this problem. Since the Apriori pruning strategy cannot identify high utility itemsets, developing an efficient algorithm is crucial for utility mining. This study proposes the Isolated Items Discarding Strategy (IIDS), which can be applied to any existing level-wise utility mining method to reduce candidates and to improve performance. The most efficient known models for share mining are ShFSM and DCG, which also work adequately for utility mining as well. By applying IIDS to ShFSM and DCG, the two methods FUM and DCG+ were implemented, respectively. For both synthetic and real datasets, experimental results reveal that the performance of FUM and DCG+ is more efficient than that of ShFSM and DCG, respectively. Therefore, IIDS is an effective strategy for utility mining.
    Relation: Data & Knowledge Engineering 64(1):198-217
    Appears in Collections:[資訊工程學系] 期刊論文

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