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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/90596


    Title: The Cyclic Model Analysis on Sequential Patterns
    Authors: Chiang, Ding-An;Wang, Cheng-Tzu;Chen, Shao-Ping;Chen, Chun-Chi
    Contributors: 淡江大學資訊工程學系
    Keywords: Association rules;data mining;frequency;sequential pattern;polynomial regression
    Date: 2009-11
    Issue Date: 2013-07-03 09:26:44 (UTC+8)
    Publisher: Piscataway: Institute of Electrical and Electronics Engineers
    Abstract: Sequential pattern mining has been used to predict various aspects of customer buying behavior for a long time. Discovered sequence reveals the chronological relation between items and provides valuable information to aid in developing marketing strategies. Nevertheless, we can hardly know whether the buying is cyclic and how long the interval between the two consecutive items in the sequential pattern is. To solve this problem, in this paper, data mining skills and the fundamentals of statistics are combined to develop a set of algorithms to unearth the cyclic properties of discovered sequential patterns. The algorithms, coupled with the sequential pattern mining process, constitute a thorough scheme to analyze and predict likely consumer behavior. The proposed algorithms are implemented and applied to test against real data collected from a consumer goods company. The experimental results illustrate how the model can be used to predict likely purchases within a certain time frame. Consequently, marketing professionals can execute campaigns to favorably impact customers' behaviors.
    Relation: Knowledge and Data Engineering, IEEE Transactions on 21(11), pp.1617-1628
    DOI: 10.1109/TKDE.2009.36
    Appears in Collections:[資訊工程學系暨研究所] 期刊論文

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