English  |  正體中文  |  简体中文  |  Items with full text/Total items : 52052/87180 (60%)
Visitors : 8897058      Online Users : 252
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: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/35010

    Title: 序列型樣之週期性間隔分析
    Other Titles: The periodical intervals analysis on sequential patterns
    Authors: 李宜靝;Lee, Yi-tian
    Contributors: 淡江大學資訊工程學系碩士班
    蔣定安;Chiang, Ding-an
    Keywords: 序列型樣;時間間隔;週期分佈;迴歸分析;Sequential Patterns;Time Interval;Periodical Distribution;Regression analysis
    Date: 2005
    Issue Date: 2010-01-11 05:53:48 (UTC+8)
    Abstract: 在處理大量交易資料分析時,我們往往透過關聯式法則(Asso-
    ciation Rules)分析所有交易項目的搭配銷售組合,並使用序列型樣(Sequential Patterns)分析顧客先後交易習性,但我們運用在產品推薦系統上僅能利用序列型樣得知產品先後購買順序,卻無法得知先後購買產品的間隔時間,以致無法在所有顯著序列資訊的產品之間根據適當時間給予最有利的產品行銷。

    本篇論文提出一套用來分析序列型樣對於時間間隔是否潛在週期之演算法,首先我們提出PDT/PDM演算法用來尋找曲線的週期性分布,並將之延伸在遞增/遞減分佈的曲線上修正為LPDT/LPDM演算法,最後我們根據序列型樣對於時間間隔的曲線分布特徵將上述方法歸納為PIM (Periodical Intervals Mining Algorithm)演算法,並將時間間隔的週期起伏挖掘出來,藉由所有產品之間序列的銷售週期比較出最佳推薦產品的銷售時間點以提供產品行銷的最有利資訊。
    In processing huge transaction data analysis, we often use Association Rules Mining and Sequential Patterns Mining techniques to discover the buying behaviors of customers. However, by sequential patterns, we are hard to find out the time intervals of related items purchased.

    In this paper, we develop a set of algorithms to analysis the periodical properties of time intervals over sequential patterns. The first, we introduce PDT/PDM algorithms to discover periodical distributions for common cases. Then, we extend them as LPDT/LPDM algorithms to overcome linearly trend components of curves. Finally, we combine those algorithms and sequential patterns’ distribution property as PIM (Periodical Intervals Mining) algorithm. By experiment, we use PIM algorithm to analysis the periodical distributions and use them to point out the best choice of products from sequential patterns by compare the periodical intervals.
    Appears in Collections:[資訊工程學系暨研究所] 學位論文

    Files in This Item:

    File SizeFormat

    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