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系統識別號 U0002-2906201017065100
中文論文名稱 客戶重覆購買行為分析
英文論文名稱 Analysis on Customer’s Repeat-Buying Behavior
校院名稱 淡江大學
系所名稱(中) 資訊工程學系碩士班
系所名稱(英) Department of Computer Science and Information Engineering
學年度 98
學期 2
出版年 99
研究生中文姓名 朱韋恩
研究生英文姓名 Wei-En Chu
學號 697410487
學位類別 碩士
語文別 中文
第二語文別 英文
口試日期 2010-06-15
論文頁數 57頁
口試委員 指導教授-蔣定安
委員-葛煥昭
委員-王鄭慈
委員-蔣定安
中文關鍵字 序列型樣  週期挖掘  重覆購買  時間資料探勘 
英文關鍵字 Sequential Patterns Mining  Periodic Mining  Repeat-Buying  Temporal Mining 
學科別分類 學科別應用科學資訊工程
中文摘要 在處理大量資料分析,利用序列型樣(Sequential Patterns)分析顧客消費資料時,只能得到產品的購買先後順序,卻無法得知產品先後購買的間隔時間,以至於無法了解此產品的消費週期,導致分析師無法在最適當的時間給予最有利的行銷。
  本論文將以時間性的資料探勘技術,建立重覆購買序列型樣的數學模型,尋找出序列型樣中各事件的次序、間隔時間,找出實際消費行為中的變化與規律關係,包括:是否具有週期關係、是否具有重覆購買週期等等。透過此模型以利分析師可以更準確的了解各產品的消費特性,在最佳的時間點擬定最有利的行銷策略,以獲得最加收益。
英文摘要 In processing huge transaction data analysis, when we use Sequential Patterns Mining techniques to discover the buying behaviors of customers, we just can only get the order of the items purchased, but we are hard to find out the time intervals of related items purchased.So that we can not know the period of the product, lead to analysts can not give the most advantageous marketing in the most appropriate time.

The aim of the this research is to develop a methodology to detect of the existence of repeat-buying behavior and discover the potential period of repeat-buying behavior. Using this model can facilitate the analysts to understand the product consumption characteristics more accurate, and let the analysts to determine the most advantageous marketing strategy in the best time, then the corresponding actions can be taken to maximize enterprise’s revenue.
論文目次 目錄
第1章 緒論 1
1.1 研究動機和目的 1
1.2 研究架構 4
第2章 文獻探討 5
2.1 序列型樣探勘 5
2.2 週期挖掘 8
2.3 CMA Algorithm 9
第3章 研究方法 20
第4章 實驗探討 26
4.1 重覆購買週期分析 30
4.2 User segmemt 探討 40
第5章 結論與未來方向 44
References 45
附錄-英文論文 48

圖目錄

圖1 產品<1004>經由RBM產生的折線圖分析報告 3
圖2 2-序列分佈形式 9
圖3 y=24-x18(sinx+1.5)曲線分佈圖 13
圖4 線性成分曲線分佈圖 15
圖5 週期成分曲線分佈圖 15
圖6 序列趨勢分佈圖 16
圖7 2-序列CMA Algorithm 虛擬碼 18
圖8 1-序列和2-序列示意圖 20
圖9 2-序列分解成1-序列示意圖 21
圖10 加入1-序列後的CMA虛擬碼 23
圖11 RBM虛擬碼 24
圖12 電信資料對2007年作AprioriALL Algorithm的結果 28
圖13 保養品資料對2001年作AprioriALL Algorithm的結果 29
圖14 <1004>2007年折線圖分析報告 31
圖15 <1004>2007年直方圖分析報告 31
圖16 <1004>2008年折線圖分析報告 32
圖17 <1004>2008年直方圖分析報告 32
圖18 <1004>2009年折線圖分析報告 33
圖19 <1004>2009年直方圖分析報告 33
圖20 2007年對2008年客戶流失趨勢圖形 35
圖21 2008年對2009年客戶流失趨勢圖形 36
圖22 2007年至2009年客戶流失趨勢圖形 38
圖23 <2001>2001年折線圖分析報告 41
圖24 族群A折線圖分析報告 42
圖25 族群B折線圖分析報告 42

表目錄

表1 客戶消費資料表 6
表2 某電信產品交易資料內容 26
表3 某電信產品交易資料內容 27
表4 RBM初始值設定 27
表5 每年的消費人數表 35
表6 2007年對2008年客戶流失趨勢表 35
表7 2008年對2009年客戶流失趨勢表 36
表8 客戶流失趨勢表 37



參考文獻 References
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