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。

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.

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<i1,i2>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

References 
[1] R. Agrawal and R. Srikant. Mining sequential patterns. Presented at Proceedings of the Eleventh International Conference on Data Engineering.
[2] R. S. Chen and Y. C. Hu. (2003, A novel method for discovering fuzzy sequential patterns using the simple fuzzy partition method. J. Am. Soc. Inf. Sci. Technol. 54(7), pp. 660-670.
[3] M. El-Sayed, C. Ruiz and E. A. Rundensteiner. FS-miner: Efficient and incremental mining of frequent sequence patterns in web logs. Presented at Proceedings of the 6th Annual ACM International Workshop on Web Information and Data Management (WIDM '04).
[4] E. Keogh, S. Lonardi and Bill 'Yuan chi' Chiu. Finding surprising patterns in a time series database in linear time and space. Presented at KDD '02: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.
[5] C. H. Lee, C. R. Lin and M. S. Chen. (2005, May). Sliding window filtering: An efficient method for incremental mining on a time-variant database. Inf Syst 30(3), pp. 227-244.
[6] M. Y. Lin and S. Y. Lee. Fast discovery of sequential patterns by memory indexing. Presented at DaWaK 2000: Proceedings of the 4th International Conference on Data Warehousing and Knowledge Discovery. 
[7] R. Srikant and R. Agrawal. Mining sequential patterns: Generalizations and performance improvements. Presented at Proc. 5th Int. Conf. Extending Database Technology, EDBT.
[8] M. J. Zaki. (2001, January). SPADE: An efficient algorithm for mining frequent sequences. Mach. Learning 42(1), pp. 31-60. 
[9] M. Garofalakis, R. Rastogi and K. Shim. (2002, Mining sequential patterns with regular expression constraints. IEEE Trans. Knowled. Data Eng. 14(3), pp. 530-552.
[10] H. Mannila, H. Toivonen and I. A. Verkamo. Discovering frequent episodes in sequences (extended abstract). Presented at In 1st Conference on Knowledge Discovery and Data Mining. 
[11] J. Pei, J. Han, B. Mortazavi-asl, H. Pinto, Q. Chen, U. Dayal and M. Hsu. (2001, PrefixSpan: Mining sequential patterns efficiently by prefix-projected pattern growth. pp. 215-224.
[12] F. Masseglia, P. Poncelet and M. Teisseire. (2003, July). Incremental mining of sequential patterns in large databases. Data and Knowledge Engineering 46(1), pp. 97-121.
[13] B. Ozden, S. Ramaswamy and A. Silberschatz. Cyclic association rules. Presented at Data Engineering, 1998. Proceedings., 14th International Conference on.
[14] Y. Li, P. Ning, X. S. Wang and S. Jajodia. Discovering calendar-based temporal association rules. Presented at Eighth International Symposium on Temporal Representation and Reasoning (TIME'01).
[15] C. Berberidis, W. G. Aref, M. Atallah, I. Vlahavas and A. K. Elmagarmid. Multiple and partial periodicity mining in time series databases. Presented at In Proc. of the 15th Euro. Conf. on Artificial Intelligence.
[16] Y. Chen. (2003, October). Discovering time-interval sequential patterns in sequence databases. Expert Syst. Appl. 25(3), pp. 343-354.
[17] Y. Chen and T. C. K. Huang. (2005, Discovering fuzzy time-interval sequential patterns in sequence databases. Systems, Man and Cybernetics, Part B, IEEE Transactions on 35(5), pp. 959-972.
[18] D. A. Chiang, S. L. Lee, C. C. Chen and M. H. Wang. (2005, March). Mining interval sequential patterns. Int J Intell Syst 20(3), pp. 359-373.
[19] M. G. Elfeky, W. G. Aref and A. K. Elmagarmid. (2005, May). Periodicity detection in time series databases. IEEE Trans. Knowled. Data Eng. 17(7), pp. 875-887.
[20] J. Han, G. Dong and Y. Yin. Efficient mining of partial periodic patterns in time series database. Presented at Proceedings of 15th International Conference on Data Engineering. 
[21] J. Han, J. Pei, B. M. Asl, Q. Chen, U. Dayal and M. C. Hsu. FreeSpan: Frequent pattern-projected sequential pattern mining. Presented at KDD '00: Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.
[22] J. Han and M. Kamber. (2006, January). Data Mining: Concepts and Techniques (the Morgan Kaufmann Series in Data Management Systems) (2nd ed.) 
[23] S. Ma and J. L. Hellerstein. Mining partially periodic event patterns with unknown periods. Presented at Proceedings of the 17th International Conference on Data Engineering. 
[24] J. Yang, W. Wang and P. S. Yu. (2003, Mining asynchronous periodic patterns in time series data. Transactions on Knowledge and Data Engineering 15(3), pp. 613-628.
[25] D. Chiang, C. Wang, S. Chen and C. Chen. (2009, nov). The cyclic model analysis on sequential patterns. Knowledge and Data Engineering, IEEE Transactions on 21(11). 
[26] 王毅新, 蔣定安, 陳劭平 & 朱奐禎 (2009). 利用 2-序列型樣作序列型樣分析. 2009 全國計算機會議論文集, 78-89.