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系統識別號 U0002-1707200522351600
中文論文名稱 序列型樣之趨勢分析
英文論文名稱 Trend Analysis on Sequential Pattern
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 93
學期 2
出版年 94
研究生中文姓名 王敬順
研究生英文姓名 Ching-Shun Wang
學號 692190977
學位類別 碩士
語文別 中文
第二語文別 英文
口試日期 2005-06-17
論文頁數 51頁
口試委員 指導教授-蔣定安
委員-王鄭慈
委員-葛煥昭
中文關鍵字 時間序列  序列型樣  趨勢偵測 
英文關鍵字 Time Series  Sequential Pattern  Trend Detection 
學科別分類 學科別應用科學資訊工程
中文摘要 本論文在探討分析序列型樣其間隔天數的銷售量之趨勢以設計一套推薦系統。經由統計產品銷售紀錄,我們以間隔天數與銷售量可以得到一個波形圖,此波形圖橫軸為間隔天數縱軸為銷售量,經由趨勢偵測可得知波形的起伏並得以瞭解消費者的購買習性,了解什麼時候可以推薦什麼產品給消費者以刺激她們的購買慾望,並期望能減少消費者購買產品之間的間隔天數,相對的亦能夠促使銷售量上升,且針對重點的消費族群和產品做推銷亦能降低成本花費,如此可以提供決策者一個不錯的建議。
英文摘要 This study confers on the trend of each day-interval’s sales volume of sequential pattern to design a recommend system. By accounting the selling records of products, we can get an oscillograph by day-interval and sales volume, this oscillograph sets the interval days as the horizontal axle and sales volume as the vertical axle. Depending on trend detection, we can know that the trend of the wave goes up or down, and we will understand the shopping habits of consumers. This can help us to know when to recommend products to customers, and hope that they will shorten the interval of days between buying products, relatively raising the sales volume. Furthermore, sales promotion on the important product toward the important consumers can reduce the production cost, so we can provide a good suggest for policymaker.
論文目次 第一章 緒論-------------------------------1
1-1 前言-------------------------------------1
1-2 研究動機與目的---------------------------3
1-3 研究架構---------------------------------5
第二章 文獻探討---------------------------6
2-1 time series mining-----------------------6
2-1.1 時間序列資料庫與序列資料庫介紹------6
2-1.2 時間序列分析------------------------7
2-1.3 趨勢分析----------------------------9
2-2 序列型樣探勘-----------------------------11
2-2.1 基本知識介紹------------------------11
2-2.2 序列型樣法則探勘--------------------14
第三章 研究方法---------------------------20
3-1 趨勢偵測---------------------------------20
3-1.1 趨勢偵測原理 -----------------------23
3-2 趨勢偵測演算法-------------------------25
3-3 趨勢線檢定-----------------------------29
第四章 實驗結果---------------------------32
4-1 測試環境與資料說明-----------------------32
4-2 資料處理---------------------------------33
4-3 趨勢檢測---------------------------------36
4-4 趨勢驗證---------------------------------39
4-5 趨勢分析研究-----------------------------42
第五章 結論與建議-------------------------49
參考文獻-----------------------------------50
參考文獻 [1] Jiawei Han and Micheline Kamber, Data Mining Concepts and Techniques, Morgan Kaufmann Publishers, 2001.
[2] A. Savasere, E. Omiecinski, S. Navathe, “An Efficient Algorithm for Mining Association Rules in Large Database," Proc. 21th VLDB, Zurich, Switzerland, pp. 432-444, 1995. [3] H. Toivonen, “Sampling large databases for association rules," In Proc. 22nd VLDB Conference, Bombay, India, pp. 134-145, Sept. 1996.
[4] J. Han, J. Pei, and Y. Yin, “Mining frequent patterns without candidate generation," Proc. of the ACM SIGMOD Int'l Conf. on Management of Data, Dallas, Texas, USA, pp. 1-12, May 2000.
[5] J.S. Park, M.S. Chen, and P.S. Yu, “Using a Hash-Based Method with Transaction Trimming for Mining Association Rules," IEEE Trans. on Knowledge and Data Eng., vol. 9, no. 5, pp. 813-825, Sept./Oct. 1997.
[6] Roberto J. Bayardo Jr., “Efficiently Mining Long Patterns from Databases," Proc. of the ACM SIGMOD Int'l Conf. On Management of Data, pp. 85-93, Seattle, Washington, June 1998.
[7] S. Brin, R. Motwani, J.D. Ullman, and S. Tsur, “Dynamic Itemset Counting and Implication Rules for Market Basket Data,"Proc. ACM SIGMOD Conf. on Management of Data, ACM Press, New York,pp. 255-264, 1997.
[8] R. Agrawal and R. Srikant, “Mining Sequential Patterns,” Proceedomgs of the 11th International Conference on Data Engineering, pp. 3-14, Mar. 1995.
[9] R. Agrawal, and et al. ”Mining Sequential Patterns: Generalizations and Performance Improvements”. Proceeding of the EDBT’96, Avignon, France, Sept. 1996.
[10] M. Garofalakis, R. Rastogi, and D. Shim, 'SPIRIT: Sequential Pattern Mining with Regurlar Expression Constraints,' Proc. 1999 Int. Conference on Very large Databases(VLDB'99), pp.223-234, Edinburgh, UK, Sep. 1999
[11] D.-A. Chiang, Y.-F. Wang, S.-L. Lee, and C.-J. Lin, "Goal-oriented sequential pattern for network banking churn analysis," Expert Systems with Applications, vol. 25, pp. 293-302, 2003.
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