臺灣博碩士論文加值系統

由於全球化、法規鬆綁、及投資限制減少等因素的影響下，各大類型企業已面臨國內外業者前所未有的強烈競爭，而企業發展型態已由過去以『產品為導向』之經營模式轉變成為以『顧客為導向』的新經營模式，對任何企業而言，顧客關係管理是一個基本的需求，而要將企業的營收與形象最佳化，更必須以客戶的需求為優先考量。以往資料探勘應用於客戶流失分析多直接以分類或分群探討，本研究嘗試以多重資料探勘技術來提高預測顧客流失的正確率，結合兩種以上的資料探勘技術，針對同一組客戶資料集來進行訓練和測試的動作並與只使用單一資料探勘技術之資料之預測正確率進行比較，以類神經網路建立預測模型，試圖找出最佳的客戶流失預測模型。

研究結果發現兩階段類神經網路建立的模型，比未經分群前處理與經過分群前處理之各模型在預測效能的表現上還要好，甚至可以達到九成三的正確率；同時在此模型中也找到一些有用的資訊，可以提供企業決策者相關重要知識，以協助制定行銷策略。

It is the fact that current domestic and foreign enterprises have been facing an unprecedented competition. The ‘product-oriented’ model has been transferred to the ‘customer-oriented’ one. This results in the importance of Customer relationship management (CRM). Customer retention is one major problem in CRM. Data mining techniques have been applied to predict the loss of customers (or customer churn). In literature, they have been proven its applicability in customer churn prediction.
In this thesis, hybrid data mining methods are developed in order to improve current single prediction models. In particular, two (different) techniques are combined in sequence, which leads to two stages of training or learning. This research considers Self-Organizing Maps (SOM) and Artificial Neural Networks (ANN) for the first component of the hybrid models respectively. Then, the second component as the prediction model to produce the final output is based on ANN. The baseline to be compared with the two hybrid models are based on the single ANN without combining with the fist component.
The experimental result shows that hybrid models outperform the baseline model in terms of prediction accuracy. In particular, ANN combined with ANN performs the best, which provides 93% prediction accuracy. in addition, it provides the lowest Type I and II error rates.

Table of Contents
Chapter 1 Introduction 1
1.1 Background 1
1.2 Motivation 3
1.3 Research objective 5
1.4 Structure of the thesis 7
Chapter 2 Literature Review 8
2.1 Data Mining 8
2.2 Classification of Data mining Techniques 9
2.2.1 Classification 9
2.2.2. Estimation 10
2.2.3. Prediction 11
2.3 Neural Network 12
2.4 Clustering 15
2.4.1 Self-organizing Map. 15
2.4.2 K-means 16
2.5 Data Mining in CRM 17
2.5.1 Customer Retention 17
Chapter 3 Experimental Setup 23
3.1 Experimental Architecture 23
3.2 Experimental Design 24
3.2 Datasets 26
3.3 Clustering and Classification Methods 28
3.4 Evaluation strategies 29
Chapter 4 Experimental Result & Analysis 31
4.1 The Baseline Model 31
4.2 SOM+ANN 32
4.3 ANN+ANN 33
4.4 Further Comparisons 34
4.4.1 The Comparison of Prediction Accuracy 35
4.4.2 Type I & II Errors 36
4.4.3 Paired t test 38
Chapter 5 Conclusion and Future work 40
5.1 Conclusion 40
5.2 Future work 41
Reference 42

Berry, M. J. A., & Linoff, G. S. (2003). Data mining Techniques: For Marketing, Sales, and Customer Support: John Wiley & Sons.
Berson, A.,Smith, S. & Thearling, K. (2000). Building Data Mining Applications for CRM ,McGraw-Hill.
Borgelt, C., & Berthold, M. R. (2002). Mining Molecular Fragments: Finding Relevant Substructures of Molecules. Paper presented at the IEEE International Conf. on Data Mining.
Catledge, L., & Pitkow, J. (1995). Characterizing browsing strategies in the World Wide Web. Computer Networks and ISDN Systems, 27, 1065-1073.
Coussement, K., & Poel, D. V. D. (2007). Churn prediction in subscription services: An application of support vector machines while comparing two parameter-selection techniques. Expert Systems with Applications, 34, 313–327.
Danielson, D. R. (2002). Web navigation and the behavioral effects of constantly visible site maps. Interacting with Computers, 14, 601-618.
Fayyad, U., Piatetsky, S. G., & Smyth, P. (1996). From data mining to knowledge discovery: An Overview In Advances in Knowledge Discovery and Data Mining. Paper presented at the AAAI/MIT Press.
Fayyad, U., & Uthurusamy, R. (1996). Data mining and knowledge discovery in databases. Communications of the ACM, 39, 24-27.
Fu, Y., Shandu, K., & Shih, M. (1999). Fast clustering of web users based on navigation pattern. Paper presented at the SCI''99/ISAS''9, Orlando, USA.
Greenberg, S., & Cockburn, A. (1999). Getting Back to Back: alternative behaviors for a Web browser’ s Back button. Paper presented at the The Fifth Annual Human Factors and the Web Conference.
Han, J., & Kamber, M. (2000). Data Mining : Concepts and Techniques. San Francisco: Morgan Kaufmann.
Han, J., & Kamber, M. (2001). Data mining: Concepts and Techniques. San Diego.
Irene, S. Y., Kwan, F. J., & Wong, H. K. (2005). An e-customer behavior model with online analytical mining for internet marketing planning. Decision Support Systems, 41, 189– 204.
Jain, A., Murty, M., & Flyn, P. (1999). Data clustering: A review. ACM Computing Surveys, 31, 264–323.
Keaveney, Susan M.,(1995) ,Customer Switching Behavior in Service Industries:An Exploratory Study. Journal of Marketing, 59 , 71-82.
Kim, H. S., & Yoon, C. H. (2004). Determinants of subscriber churn and customer loyalty in the Korean mobile telephony market. Telecommunications Policy, 28, 751–765.
Kim, M., Park, M., & Jeong, D. (2004). The effects of customer satisfaction and switching barrier on customer loyalty in Korean mobile telecommunication services. Telecommunications Policy, 28, 145-159.
Kohonen, T. (1990). The self-organizing map. IEEE, 1464-1480.
Lefever, E., Hoste, V., & Fayruzov, T. (2007). AUG: A combined classification and clustering approach for web people disambiguation. Paper presented at the The 4th International Workshop on Semantic Evaluations.
Li, H. F., Lee, S. Y., & Shan, M. K. (2004). On Mining Webclick Streams for Path Traversal Patterns. Paper presented at the WWW 2004.
Li, H. F., Lee, S. Y., & Shan, M. K. (2006). DSM-PLW: Single-pass mining of path traversal patterns over streaming Web click-sequences. Computer Networks, 50, 1474–1487.
Lia, C. T., & Tan, Y. H. (2006). Adaptive control of system with hysteresis using neural networks. Journal of Systems Engineering and Electronics 17, 163-167
MacQueen, J. B. (1967). Some methods for classification and analysis of multivariate observations. Paper presented at the The 5th Berkeley Symposium on Mathematical Statistics and Probability.
Mitra, S., & Acharya, T. (2003). Data mining : Multimedia, Soft Computing, and Bioinformatics: John Wiley & Sons.
Piatetsky, S. P., & Frawley, W. J. (1991). Knowledge Discovery in Databases. Paper presented at the AAAI/MIT Press.
Roiger, R. J., & Michael, W. G. (2003). Data mining: A tutorial-Based Primer: Addison-Wesley.
Rygielski, C., Wang, J. C., & Yen, D. C. (2002). Data mining techniques for customer relationship management. Technology in Society, 24, 483-502.
Savasere, A., Omiecinski, E., & Navathe, S. (1995). An efficient algorithm for mining association rules in large databases. Paper presented at the International Conference on Very Large Databases.
Tauscher, L. M., & Greenberg, S. (1997). How people revisit Web pages: empirical findings and implications for the design of history mechanisms. International Journal of Human-Computer Studies, 47, 94-137.
Theusinger, C., & Huber, K. P. (2000). Analyzing the Footsteps of Your Customer.
Tou, J. T., & Gonzalez, R. C. (1974). Pattern Recognition Principles: Addison-Wesley.
Wagstaff, K., Cardie, C., Rogers, S., & Schroedl, S. (2001). Constrained K-Means clustering with background knowledge. Paper presented at the The 18th International Conference on Machine Learning.
Zhang, X., Edwards, J., & Harding, J. (2007). Personalised online sales using web usage data mining. Computers in Industry, 58, 772–782.

Berry, M. J. A., & Linoff, G. S. (2003). Data mining Techniques: For Marketing, Sales, and Customer Support: John Wiley & Sons.
Berson, A.,Smith, S. & Thearling, K. (2000). Building Data Mining Applications for CRM ,McGraw-Hill.
Borgelt, C., & Berthold, M. R. (2002). Mining Molecular Fragments: Finding Relevant Substructures of Molecules. Paper presented at the IEEE International Conf. on Data Mining.
Catledge, L., & Pitkow, J. (1995). Characterizing browsing strategies in the World Wide Web. Computer Networks and ISDN Systems, 27, 1065-1073.
Coussement, K., & Poel, D. V. D. (2007). Churn prediction in subscription services: An application of support vector machines while comparing two parameter-selection techniques. Expert Systems with Applications, 34, 313–327.
Danielson, D. R. (2002). Web navigation and the behavioral effects of constantly visible site maps. Interacting with Computers, 14, 601-618.
Fayyad, U., Piatetsky, S. G., & Smyth, P. (1996). From data mining to knowledge discovery: An Overview In Advances in Knowledge Discovery and Data Mining. Paper presented at the AAAI/MIT Press.
Fayyad, U., & Uthurusamy, R. (1996). Data mining and knowledge discovery in databases. Communications of the ACM, 39, 24-27.
Fu, Y., Shandu, K., & Shih, M. (1999). Fast clustering of web users based on navigation pattern. Paper presented at the SCI''99/ISAS''9, Orlando, USA.
Greenberg, S., & Cockburn, A. (1999). Getting Back to Back: alternative behaviors for a Web browser’ s Back button. Paper presented at the The Fifth Annual Human Factors and the Web Conference.
Han, J., & Kamber, M. (2000). Data Mining : Concepts and Techniques. San Francisco: Morgan Kaufmann.
Han, J., & Kamber, M. (2001). Data mining: Concepts and Techniques. San Diego.
Irene, S. Y., Kwan, F. J., & Wong, H. K. (2005). An e-customer behavior model with online analytical mining for internet marketing planning. Decision Support Systems, 41, 189– 204.
Jain, A., Murty, M., & Flyn, P. (1999). Data clustering: A review. ACM Computing Surveys, 31, 264–323.
Keaveney, Susan M.,(1995) ,Customer Switching Behavior in Service Industries:An Exploratory Study. Journal of Marketing, 59 , 71-82.
Kim, H. S., & Yoon, C. H. (2004). Determinants of subscriber churn and customer loyalty in the Korean mobile telephony market. Telecommunications Policy, 28, 751–765.
Kim, M., Park, M., & Jeong, D. (2004). The effects of customer satisfaction and switching barrier on customer loyalty in Korean mobile telecommunication services. Telecommunications Policy, 28, 145-159.
Kohonen, T. (1990). The self-organizing map. IEEE, 1464-1480.
Lefever, E., Hoste, V., & Fayruzov, T. (2007). AUG: A combined classification and clustering approach for web people disambiguation. Paper presented at the The 4th International Workshop on Semantic Evaluations.
Li, H. F., Lee, S. Y., & Shan, M. K. (2004). On Mining Webclick Streams for Path Traversal Patterns. Paper presented at the WWW 2004.
Li, H. F., Lee, S. Y., & Shan, M. K. (2006). DSM-PLW: Single-pass mining of path traversal patterns over streaming Web click-sequences. Computer Networks, 50, 1474–1487.
Lia, C. T., & Tan, Y. H. (2006). Adaptive control of system with hysteresis using neural networks. Journal of Systems Engineering and Electronics 17, 163-167
MacQueen, J. B. (1967). Some methods for classification and analysis of multivariate observations. Paper presented at the The 5th Berkeley Symposium on Mathematical Statistics and Probability.
Mitra, S., & Acharya, T. (2003). Data mining : Multimedia, Soft Computing, and Bioinformatics: John Wiley & Sons.
Piatetsky, S. P., & Frawley, W. J. (1991). Knowledge Discovery in Databases. Paper presented at the AAAI/MIT Press.
Roiger, R. J., & Michael, W. G. (2003). Data mining: A tutorial-Based Primer: Addison-Wesley.
Rygielski, C., Wang, J. C., & Yen, D. C. (2002). Data mining techniques for customer relationship management. Technology in Society, 24, 483-502.
Savasere, A., Omiecinski, E., & Navathe, S. (1995). An efficient algorithm for mining association rules in large databases. Paper presented at the International Conference on Very Large Databases.
Tauscher, L. M., & Greenberg, S. (1997). How people revisit Web pages: empirical findings and implications for the design of history mechanisms. International Journal of Human-Computer Studies, 47, 94-137.
Theusinger, C., & Huber, K. P. (2000). Analyzing the Footsteps of Your Customer.
Tou, J. T., & Gonzalez, R. C. (1974). Pattern Recognition Principles: Addison-Wesley.
Wagstaff, K., Cardie, C., Rogers, S., & Schroedl, S. (2001). Constrained K-Means clustering with background knowledge. Paper presented at the The 18th International Conference on Machine Learning.
Zhang, X., Edwards, J., & Harding, J. (2007). Personalised online sales using web usage data mining. Computers in Industry, 58, 772–782.