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Student Number 944203006
Author Min-hao Kuo()
Author's Email Address 944203006@cc.ncu.edu.tw
Statistics This thesis had been viewed 1783 times. Download 1342 times.
Department Information Management
Year 2006
Semester 2
Degree Master
Type of Document Master's Thesis
Language English
Title Discovering RFM sequential patterns from customers purchasing data
Date of Defense 2007-06-25
Page Count 44
Keyword
  • constraint-based mining
  • data mining
  • RFM
  • segmentation
  • sequential patterns
  • Abstract Sequential pattern mining is an important data mining task of discovering time-related behaviors in sequence databases. Sequential pattern mining technology has been applied in many domains, including web-log analysis, the analyses of customer purchase behavior, process analysis of scientific experiments, medical record analysis, etc. Although a lot of works have been done to sequential pattern mining, most of them discover sequential patterns only based on frequency. Due to this reason, the minimum support must be set to a low value; otherwise, a lot of valuable patterns may not be found. Unfortunately, doing so may cause combinatorial explosion, producing too many rules. To resolve this dilemma, this research uses the concept of Recency, Frequency and Monetary (RFM), which is usually used by marketing researchers to do customer or market segmentation, to find valuable sequential patterns. The proposed algorithm RFM-Apriori modifies the traditional sequential pattern mining algorithm-Apriori, so that, except the frequency, we also consider two additional constraints, the last purchasing time (Recency) and purchasing money (Monetary), to discover the RFM-patterns. The advantage of considering these two additional factors is that this can ensure all patterns are recently active and profitable. The empirical evaluation shows that the proposed method is computationally efficient and can offer users an effective means to discover valuable patterns.
    Table of Content CHAPTER 1@INTRODUCTION1
    CHAPTER 2@RELATED WORKS6
    2.1@SEQUENTIAL PATTERN MINING6
    2.2@RFM7
    CHAPTER 3@DEFINITION9
    CHAPTER 4@ALGORITHMS13
    4.1@CANDIDATE GENERATION16
    4.2@COUNTING SUPPORT BY TRAVERSING AN INVERSE CANDIDATE TREE18
    4.2.1@Inverse Candidate Tree20
    4.2.2@Counting Support for Candidates21
    4.3@RFM-APRIORI ALGORITHM - EXAMPLE22
    CHAPTER 5@ EXPERIMENTS27
    5.1@SYNTHETIC DATA GENERATION AND REAL-LIFE DATASET27
    5.2@PERFORMANCE EVALUATION29
    CHAPTER 6@ CONCLUSION39
    REFERENCE41
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    Advisor
  • Yen-Liang Chen(ۨ})
  • Files
  • 944203006.pdf
  • approve immediately
    Date of Submission 2007-07-02

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