臺灣博碩士論文加值系統

摘要
由於WEB2.0的興起，加上台灣上網人數增加，台灣的電子商務於網路泡沫化之後再度興盛，更多消費者願意於線上購物，使得購物網站累積為數可觀之會員數目。實務上，經營管理者對這些會員並未能充分分析其消費行為，且會員資料之分類亦採用少數準則。由於會員分類不夠明確，行銷手法無法確實傳達到潛在消費者，致使回購率無法提高，進而影響經營績效。對購物網站經營管理者而言，若能在會員資料庫中迅速地找出核心顧客群，並依據其屬性分別訂定不同之行銷手法，將可提高銷售額，進而提升市佔率，並使其行銷策略收事半功倍之效。
本研究之目的為下述三點：（1）歸納台灣電子商務的會員機制與發展現況。（2）利用決策樹建構適用於台灣電子商務業者自動會員分類模型。（3）探討自動會員分類模型之分類準確度。
本研究利用模擬會員資料庫，依照會員之屬性，如上網時數、購買金額、購買次數等，運用決策樹C4.5演算法建構出4個電子商務網站會員分類模型，並進行模型修正，以提高自動分類之效率。此模型可協助電子商務網站業者自動分類其會員，並能更有效率地找出核心顧客族群，以制訂最有效益的行銷策略，藉此提升其競爭力。
研究結果發現：（1）利用決策樹分類法可實際找出分類電子商務網站會員之重要屬性。（2）利用決策樹方法可建構出自動會員分類模型。（3）決策樹經過修剪過後可以減少決策法則複雜度但分類準確度會下降。
關鍵字：自動分類、決策樹、電子商務

Due to the rise of Web 2.0 and the increase of Internet users in Taiwan, e-commerce has begun to prevail again in Taiwan after the dot-com crisis. Now, more and more consumers are willing to shop online, resulting in a huge growth of online shopping websites. In practice, administrators of these websites seldom sufficiently analyze the consumer behavior of their members and classify them only by a few criteria. Without accurate classification of members, marketing cannot effectively reach potential consumers. As a result, members’ repurchase rate can hardly be improved, further affecting business performance. For administrators of online shopping websites, if they can quickly find core customers from the member database and adopt effective marketing strategies based on their attributes, they can enhance their sales performance, increase their market share, and double the effect of the adopted marketing strategies.
The objectives of this study were as follows:
1. To explore the current member management mechanisms and development of e-commerce in Taiwan.
2. To use decision tree to construct an automatic member classification model for e-commerce service providers in Taiwan.
3. To investigate the classification accuracy of the proposed model.
Based on member attributes of a simulated member database, such as hours of using Internet, expenditure, and shopping frequency, four e-commerce member classification models were constructed using decision tree C4.5 algorithm. The models were further modified to enhance the efficiency of automatic classification. These models could assist e-commerce service providers to automatically classify their members and more efficiently find core customer groups, so as to set up effective marketing strategies and thus enhance their competitiveness.
The research findings included:
1. Through application of decision tree, important attributes could be derived to classify members of e-commerce websites.
2. An automatic member classification model could be constructed by using decision tree.
3. Through trimming of the decision tree, complex of the decision rules could be reduced, but accuracy of the classification model could be decrease.

目 錄
摘要 ii
Abstract iii
誌謝 v
表 目 錄 vii
圖 目 錄 viii
一、 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 3
1.3 研究方法 3
1.4 研究流程與步驟 3
1.5 研究架構 5
二、 文獻探討 6
2.1 台灣電子商務產業概況與會員制度 6
2.2 資料探勘 12
2.3 分類法 21
三、 研究方法 28
3.1 C4.5決策樹演算法 28
3.2決策法則 34
3.3決策樹軟體之介紹 34
四、 資料分析 36
4.1資料來源與整理 36
4.2前測問卷發放與修改 39
4.3 正式問卷發放與分析 39
4.4 建構會員自動分類系統 44
五、 結論 60
5.1 結論 60
5.2 後續研究建議 62
參考文獻 64

表 目 錄
表1. 各項資料探勘技術可達成的功能 17
表2. 各決策樹演算法之比較 27
表3. 問卷會員屬性資料表 37
表4. 會員自動分類模型前測分析資料表 38
表5. 會員自動分類模型分析資料表 40
表6. 2008年台灣線上購物族群樣本資料表 43
表7. 上網活動主要是瀏覽購物網站之會員自動分類模型之決策法則 49
表8. 購物內容主要為衣服與美容產品會員自動分類模型之決策法則 53
表9. 購物內容主要為3C、家電與遊戲產品會員自動分類模型之決策法則 56
表10. 購物內容主要為書籍與食品產品會員自動分類模型之決策法則 59


圖 目 錄
圖1. 研究步驟 5
圖2. 電子商務應用架構 7
圖3. 整合各領域之資料探勘 15
圖4. C4.5連續屬性選擇過程 33
圖5. C4.5決策樹相關參數設定 45
圖6. Weka訓練及測試模式綜合方式參數設定 46
圖7. 上網活動主要是瀏覽購物網站之會員自動分類模型 47
圖8. 決策樹圖例介紹 48
圖9. 購物內容主要為衣服和美容產品之會員自動分類原型 50
圖10. 購物內容主要為衣服與美容產品之會員自動分類模型 52
圖11. 購物內容主要為3C、家電與遊戲產品之會員自動分類原型 54
圖12. 購物內容主要為3C、家電與遊戲產品會員自動分類模型 55
圖13. 購物內容主要為書籍與食品之會員自動分類原型 57
圖14. 購物內容主要為書籍與食品之會員自動分類模型 58

1.史書華（2008）。網路開店，聽七年級的！，Cheers， 94。
2.全球華文行銷知識庫（2008）。張大眼睛看網路購物現況，凱絡媒體週報，461。
3.全球華文行銷知識庫（2009）。2009年企業網站50強和網路公僕20強探討，數位時代，2009年一月刊。
4.何宏儒（2008）。「資策會：台灣經常上網人口1014萬普及率44%」，中央日報網路報，http：//www.cdnews.com.tw/cdnews_site/docDetail.jsp? coluid=115& docid=100550218。
5.吳盈宜（2000）。歸納學習法中決策樹連續屬性分割點之選擇，國立成功大學資訊管理研究所碩士論文，。
6.陳垂呈、利益多（2002）。應用資料探勘技術發掘最適性之線上拍賣競標者，運籌研究集刊第一期。
7.匿名網路作家（2009）。Sonet-停止電子商務平台服務－台灣新的電子商務時代，http://terrylogin.blogspot.com/2009/02/soneet.html。
8.張淑珍、趙景明（2005）。利用一次性的SQL改良決策樹建立信用卡審核之信用評等，東吳大學商學院資訊科學研究所碩士論文。
9.辜樹仁（2008）。阿里巴巴台灣開門，天下雜誌，403。
10.電子商務學院（2007）。網站經營：電子商店經營模式官方定義，http：//www.web-time.com.tw/details/ec.aspx?id=228。
11.賴士奇、吳嘉哲、劉揚凱、楊子蕙譯，Swift, R. S.著（2000）。深化顧客關係管理，第1版，遠擎管理顧問，臺北。
12.盧昭燕（2009）。女性網購族，救了台灣零售業，天下雜誌，414。
13.顏博文、李維平（2003）。應用資料探勘技術分析學生選課特性與學業表現，中原大學資訊管理學研究所碩士論文。
14.Weka官方論壇（2009）。Java語言版開放源代碼數據挖掘軟體，http://www.cs.waikato.ac.nz/ml/weka/，於2009年5月28日擷取。
15.Agrawal, R., Lin, K., Sawhney, H. S., and Shim, K. (1995). Fast Similarity Search in the Presence of Noise, Scaling and Translation in Time-Series Databases, in Proceedings of the 21st International Conference on Very Large Databases, Zurich, Switzerland, 490-501.
16.Aha, D. W., Kibler, D., and Albert, M. K. (1991). Instance-Based Learning Algorithms, Machine Learning, 6, 37-66.
17.Berry, M. J. A., and Linoff, G. S. (2004). Data Mining Techniques for Marketing, Sales, and Customer Support, 2nd ed., New York: John Wiley.
18.Brachman, R. J., Khabaza, T., Kloesgen, W., Piatetsky-Shapiro, G., and Simoudis, E. (1996). Mining Business Databases, Communication of the ACM, 39, 11, 42-48.
19.Breiman, L., Friedman, J. H., Olshen, R. A., and Stone, C. J. (1984). Classification and Regression Trees, The Wadsworth Statistics/Probability Series, Belmont, CA, USA.
20.Carbonell, J. G., Yang, Y., Brown, R. D., Pierce, T., Archibald, B. T., and Liu, X. (1999). Learning Approaches for Detecting and Tracking News Event, IEEE Intelligent Systems and Their Applications, 32-43.
21.Cameron-Jones, R. M. (1992). Minimum Description Length Instance-Based Learning, in Proceedings of the 15th Australian Joint Conference on Artificial Intelligence, Hobart, Australia: World Scientific, 368-373.
22.Cheeseman, P., and Stutz, J. (1996). Bayesian Classification (AutoClass): Theory and Results, Advances in Knowledge Discovery and Data Mining, American Association for Artificial Intelligence/The MIT Press, 153-180, CA, USA.
23.Cost, S., and Salzberg, S. (1993). A Weighted Nearest Neighbor Algorithm for Learning with Symbolic Features, Machine Learning, 10, 57-78.
24.Kosiur, D. R. (1997). Understanding Electronic Commerce, Microsoft Press, P.4.
25.Fayyad, U., Piatetsky-Shapiro, G., and Smyth, P. (1996). From Data Mining to Knowledge Discovery in Databases, AI Magazine, 37-54.
26.Han, J., and Kamber, M. (2006). Data Mining: Concepts and Techniques, 2nd ed., Morgan Kaufmann.
27.Holland, J. H. (1975). Adaptation in Natural and Artificial System, Ann Arbor, MI: University of Michigan Press.
28.Hui, S. C., and Jha, G. (2000). Data Mining for Customer Service Support, Information and Management, Vol. 38：1-13.
29.Kass, G. (1980). An Exploratory Technique for Investigating Large Quantities of Categorical Data, Applied Statistics, 29, 2, 119-127.
30.Kalakota, R., and Whinston, A. B. (1997). Electronic Commerce: A Manager’s Guide, MA: Addison-Wesley.
31.Laudon, K. C., and Laudon, J. P. (2000). Management Information Systems, 6th ed., Upper Saddle River, NJ: Prentice Hall.
32.Mehta, M., Agrawal, R., and Rissanen, J. (1996). SLIQ: A Fast Scalable Classifier for Data Mining, Proceedings of the 5th International Conference on Extending Database 73 Technology, 18-32, Avignon, France.
33.Michalewicz, Z. (1996). Genetic Algorithm + Data structures= Evolution Programs, 3rd edition, Springer Verlag.
34.Murthy, S. K. (1998). Automatic Construction of Decision Trees from Data: A Multi- Disciplinary Survey, Data Mining and Knowledge Discovery, 2, 4, 345-389.
35.Quinlan, J. R. (1986). Induction of Decision Trees, Machine Learning, 1, 81-106.
36.Quinlan, J. R. (1993). C4.5: Programs for Machine Learning, Morgan Kaufmann, San Francisco, CA, USA.
37.Quinlan, J. R. (1996). Improved Use of Continuous Attributes in C4.5, Journal of Artificial Intelligence Research, 4, 77-90.
38.Ramkumar, G. D., and Swami, A., (1998). Clustering Data without Distance Functions, IEEE Technical Committee on Data Engineering, 9-14.
39.Rastogi, R., and Shim, K. (1998). PUBLIC: A Decision Tree Classifier that Integrates Building and Pruning, Proceedings of the 24th International Conference on Very Large Databases, 404-415, New York, USA.
40.Shafer, J., Agrawal R., and Mehta, M. (1996). SPRINT: A Scalable Parallel Classifier for Data Mining, in Proceedings of the 22nd International Conference on Very Large Databases, Bombay, India, 544-555.
41.Vellido, A., Lisboa, P. J. G. and Vaughan, J. (1999). Neural Networks in Business: A Survey of Applications (1992-1998), Expert Systems with Applications, 17, 51-70.