English  |  正體中文  |  简体中文  |  全文筆數/總筆數 : 51258/86283 (59%)
造訪人次 : 8003874      線上人數 : 47
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library & TKU Library IR team.
搜尋範圍 查詢小技巧:
  • 您可在西文檢索詞彙前後加上"雙引號",以獲取較精準的檢索結果
  • 若欲以作者姓名搜尋,建議至進階搜尋限定作者欄位,可獲得較完整資料
  • 進階搜尋
    請使用永久網址來引用或連結此文件: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/90596

    題名: The Cyclic Model Analysis on Sequential Patterns
    作者: Chiang, Ding-An;Wang, Cheng-Tzu;Chen, Shao-Ping;Chen, Chun-Chi
    貢獻者: 淡江大學資訊工程學系
    關鍵詞: Association rules;data mining;frequency;sequential pattern;polynomial regression
    日期: 2009-11
    上傳時間: 2013-07-03 09:26:44 (UTC+8)
    出版者: Piscataway: Institute of Electrical and Electronics Engineers
    摘要: 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.
    關聯: Knowledge and Data Engineering, IEEE Transactions on 21(11), pp.1617-1628
    DOI: 10.1109/TKDE.2009.36
    顯示於類別:[資訊工程學系暨研究所] 期刊論文


    檔案 描述 大小格式瀏覽次數



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