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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/65114

    Title: 以基因演算法探討 GSP 參數之研究;Tuning GSP parameters with GA
    Authors: 鄭洧奇;Jheng,Wei-ci
    Contributors: 企業管理學系
    Keywords: 序列模式挖掘;GSP法;基因演算法;Sequential pattern mining;GSP;GA
    Date: 2014-07-16
    Issue Date: 2014-10-15 14:40:54 (UTC+8)
    Publisher: 國立中央大學
    Abstract: 中文摘要
    GSP 法即是利用時間戳記的屬性,來找到具有序列模式的產品組合。然而,GSP 法
    結果不穩定。本研究使用參數庫的設置結合 GSP 法以及基因演算法,透過不斷地演化改

    關鍵字:序列模式挖掘、GSP 法、基因演算法;Tuning GSP parameters with GA

    In data mining, association rules can be shown when customers buy products, which
    products will be purchased at the same time. Scholars use this feature to develop market basket
    analysis to formulate marketing strategies for business.
    As we know, the data are changing all the time. When new data generate, the old data will
    be replaced. In the database, time become a very important attribute. And new data mining
    method have been proposed, called generalized sequential patterns (GSP).
    GSP uses time stamp to find the product portfolio with sequential patterns. However, the
    GSP parameter is user-defined. The result of the operation may be unstable, because of the
    parameter setting incorrectly. Tuning the parameters used in this study combined GSP and
    genetic algorithm (GA) to improve the result continuously, to find the appropriate parameters.
    In the experiment, we use a medium-sized supermarket verify the results and found that
    after comparing with random input parameters, the parameters of the proposed method found
    significantly better than a random set of parameters.

    Keywords:Sequential pattern mining、GSP、GA
    Appears in Collections:[企業管理研究所] 博碩士論文

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