English  |  正體中文  |  简体中文  |  Items with full text/Total items : 62796/95837 (66%)
Visitors : 3640630      Online Users : 367
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library & TKU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/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

    Files in This Item:

    File Description SizeFormat
    index.html0KbHTML361View/Open

    All items in 機構典藏 are protected by copyright, with all rights reserved.


    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library & TKU Library IR teams. Copyright ©   - Feedback