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題名 選擇商業應用資料探勘方法之框架
A Framework for Selecting Data Mining Method in Business Application
作者 陳庭鈞
Chen,Tin Jiun
貢獻者 諶家蘭<br>季延平
Seng,Jia Lang<br>Chi,Yen Ping
陳庭鈞
Chen,Tin Jiun
關鍵詞 資料探勘
商業應用
選擇方法
資料探勘演算法
Data mining
Business application
Selection method
Data mining algorithm
日期 2005
上傳時間 18-Sep-2009 14:30:17 (UTC+8)
摘要 由於資訊科技的進步與網路的普及，企業得以收集與儲存大量的資料。使用資訊工具來協助資料處理、資訊擷取、以及產生知識已然變成企業的重要課題之一，所以如何良好運用資料探勘工具成為使用者關注的焦點。由於並非每一個使用者對於資料探勘的原理都有充分的了解，所以如何從探勘工具提供的功能中選用最佳的解決方案並不容易。如果對於探勘結果不滿意而需要調整軟體邏輯，與IT人員的協商溝通卻又曠日費時。

為了解決這個問題，本研究提出一個演算法選擇方法，藉由分析商業應用的內容，來自動對應到特定的資料探勘方法與演算法，讓選擇演算法的過程更為快速、更系統化，提升利用資料探勘工具解決商業問題的效率。
Due to the information technology improvement and the growth of internet, companies are able to collect and to store huge amount of data. Using data mining technology to aid the data processing, information retrieval and knowledge generation process has become one of the critical missions to enterprise, so how to use data mining tools properly is users’ concern. Since not every user completely understand the theory of data mining, choosing the best solution from the functions which data mining tools provides is not easy. If user is not satisfied with the outcome of mining, communication with IT employees to adjust the software costs lots of time.

To solve this problem, a selection model of data mining algorithms is proposed. By analyzing the content of business application, user’s requirement will map to certain data mining category and algorithm. This method makes algorithm selection faster and reasonable to improve the efficiency of applying data mining tools to solve business problems.
參考文獻 1. Agrawal, R., Imielinski, T. and Swami, A. (1993, May). Mining association rules between sets of items in large databases. SIGMOD, Washington.
2. Ahmed, S. R. (2004). Applications of Data Mining in Retail Business. Proceedings of the International Conference on Information Technology: Coding and Computing.
3. Ahn, J. H., and Ezawa, K. J. (1997). Decision support for eeal-time telemarketing operations through bayesian network learning, Decision Support Systems. 21, 17-27
4. Ahn, J. Y., Kim S. K. and Han, K. S. (2003). On the design concepts for CRM systems. Industrial Management and Data System. 103(5), 324-331.
5. Anand, S. S., Patrick, A. R., Hughes, J. G., and Bell, D. A. (1998). A data mining methodology for cross-sales. Knowledge-Based Systems. 10, 449-461.
6. Apte, C., Liu, Bing, Pednault, E. P. D. and Smyth P. (2002). Business applications of data mining. Communications of the ACM archive. 45(8), 49-53.
7. Arshadi, N. and Jurisica, I. (2005). Data mining for case-based reasoning in high-dimensional biological domains. IEEE transactions on knowledge and data engineering. 17(8), 1127-1137.
8. Bansal, K., Vadhavkar, S. and Gupta, A. (1998). Neural Networks Based Data Mining Applications for Medical Inventory Problems. International Journal of Agile Manufacturing, 1(2), 187-200.
9. Berry, M. J. A. and Linoff G. (1997). Data mining techniques: for marketing, sales, and customer support. John Wiley & Sons Press.
10. Bose, R. (2006). Intelligent technologies for managing fraud and identify Theft. Proceedings of the Third International Conference on Information Technology: New Generation. 446-451.
11. Bose, R. (2002). Customer relationship management: key components for IT success. Industrial Management & Data Systems. 102(2), 89-97.
12. Brachman, R. J., Khabasa, T., Kloesgen, W., Piatetsky-Shapiro, G., and Simoudis, E. (1996). Mining business database. Communication of ACM. 39(11), 42-48.
13. Brause, R., Langsdorf, T. and Hepp, M. (1999). Neural Data Mining for Credit Card Fraud Detection. Proceedings of the 11th IEEE International Conference on Tools with Artificial Intelligence, Chicago. 103-106
14. Chan, P. K., Fan, W., Prodromidis, A. L. and Stolfo, S.J. (1999). Distributed data mining in credit card fraud detection. Intelligent Systems and Their Applications. 14(6), 67-74
15. Chaudhuri, S. and Dayal, U. (1997, March). An overview of data warehousing and OLAP technology. ACM SIGMOD Record. 26(1), 65 – 74.
16. Chen, M. S., Han, J. and Yu, P. S. (1996). Data mining: an overview from database perspective. IEEE Transactions on Knowledge and Data Engineering. 8(6), 866-883
17. Chen, R. S., Wu, R. C. and Chen J. Y. (2005) Data mining application in customer relationship management of credit card business. Proceedings of the 29th Annual International Computer Software and Applications Conference (COMPSAC’05). 2, 39-40.
18. Chen, Y. and Hu, L. (2005). Study on data mining application in CRM system based on insurance trade. Proceedings of the 7th international conference on electronic commerce, Xi`an, China. 839-841.
19. Davidson, A. and Simonetto, M., (2005). Pricing strategy and execution: an overlooked way to increase revenues and profits. STRATEGY & LEADERSHIP. 33(6), 25-33.
20. Emili, T. A., (2004). Cost Efficiency and Product Mix Clusters across the Spanish Banking Industry. Review of Industrial Organization. 20, 163-181.
21. Farquhar, J. D. (2004). Customer retention in retail, financial services: an employee perspective. The International Journal of Bank Marketing. 22(2), 86-99.
22. Fayyad, U., Piatetsky-Shapiro, G. and Smyth, P. (1996). The KDD process for extracting useful knowledge from volumes of data. COMMUNICATIONS OF THE ACM. 39(11), 27-34
23. Fayyad, U., Piatetsky-Shapiro, G., Smyth, P., and Uthurusamy, R. (1996). Advances in knowledge discovery and data dining. Cambridge: AAAI/MIT Press.
24. Ferdousi, Z. and Maeda, A. (2006). Unsupervised outlier detection in time series data. Proceedings of the 22nd International Conference on Data Engineering Workshops (ICDEW`06). 51-56.
25. Fish, K. E. and Segall, R. S. (2004) A visual analysis of learning rule effects and variable importance for neural networks in data mining operations. Kybernetes. 33(7), 1127-1142
26. Forcht, K. A. and Cochran, K. (1999). Using data mining and datawarehousing techniques. Industrial Management & Data Systems. 99(5), 18-19.
27. Gardner, M. and Bieker, J. (2000). Data mining solves tough semiconductor manufacturing problems. Conference on Knowledge Discovery in Data, Boston. ACM Press, 376 – 383.
28. Gargano, M. L. and Raggad, B. G. (1999). Data mining – a powerful information creating tool. OCLC Systems & Services. 15(2), 81-90.
29. Gavrilov, M., Anguelov, D., Indyk, P. and Motwani, R. (2000). Mining The Stock Market: Which Measure Is Best ?. Conference on Knowledge Discovery in Data, Boston, MA USA. 487-496.
30. Goebel, M. and Gruenwald, L. (1999). A survey of data mining and knowledge discovery software tools. ACM SIGKDD Explorations Newsletter. 1(1), 20-33.
31. Han, J. and Kamber, M. (2001). Data mining: concepts and techniques. Academic Press.
32. Lee, S. J. and Siau, K. (2001). A review of data mining techniques. Industrial Management & Data Systems. 101(1), 41-46.
33. Lee, T. S., Chiu C. C., Chou, Y. C. and Lu, C. J. (2006). Mining the customer credit using classification and regression tree and multivariate adaptive regression splines. Computational Statistics & Data Analysis. 50, 1113-1130.
34. Lejeune, M. A. P. M. (2001). Measuring the impact of data mining on churn management. Internet Research: Electronic Networking Applications and Policy. 11(5), 375-387.
35. Lim, T. S., Loh, W. Y., and Shih, Y. S. (2000). A Comparison of Prediction Accuracy, Complexity, and Training Time of Thirty-Three Old and New Classification Algorithms. Machine Learning. 40, 203-228.
36. Lu, H. J., Feng, L., and Han, J. (2000). Beyond intratransaction association analysis- mining multidimensional intertransaction association rules. ACM Transactions of Information Systems. 18(4), 423-454.
37. Lu, H. J., Han, J., and Feng, L. (1998). Stock movement prediction and N-dimensional inter-transaction association rules. ACM SIGMOD Workshop on Research Issues on Data Mining and Knowledge Discovery, Seattle. 12.1-12.7.
38. Mannila, H. (2000). Theoretical Frameworks for Data Mining. ACM SIGKDD Explorations Newsletter. 1(1), 30-32.
39. Min, Hokey, Min, Hyesung, and Emam, Agmed. (2002). A data mining approach to developing the profiles of hotel customers. International Journal of contemporary Hospitality Management. 14(6), 274-285.
40. Netessine S., Savin, S., and Xiao w. (2004) Revenue Management through Dynamic Cross-Selling in E-commerce Retailing. Working paper.
41. Piatetsky-Shapiro, G. and Frawley W. J. (1991). Knowledge discovery in databases. AAAI/MIT Press.
42. Pitta, D., A. (1998). Marketing one-to-one and its dependence on knowledge discovery in databases. Journal of Consumer Marketing. 15(5), 468-480.
43. Pritscher, L. and Feyen, L (2001). Data Mining And Strategic Marketing In The Airline Industry. Data Mning FOR Marketing Applications.
44. Povinelli, R. J. and Feng. X. (1999). Data mining of multiple nonstationary time series. Proceedings of Artificial Neural Networks in Engineering. St. Louis, Missouri, 511-516.
45. Silberschatz, A., Stonebraker, M., and Ullman, J. (1995). Database research: Achievements and opportunities into the 21st century. SIGMOD Record. 25(1), 52-63.
46. Spangler, W. E., Gal-Or, M., and May J. H. (2003). Using data mining to profile TV viewers. Communication of ACM. 46(12), 67-72.
47. Vellido, A., Lisboa, P. J. G. and Vaughan, J. (1999). Neural network in business: a survey of applications (1992-1998). Expert Systems with Applications. 17, 51-70.
48. West, D. (2000). Neural network credit scoring models. Computers & Operations Research. 27, 1131-1152.
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50. 洪順慶(2001)。行銷管理。第二版。台北，新陸書局。
描述 碩士
國立政治大學
資訊管理研究所
93356031
94
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0093356031
資料類型 thesis
dc.contributor.advisor 諶家蘭<br>季延平zh_TW
dc.contributor.advisor Seng,Jia Lang<br>Chi,Yen Pingen_US
dc.contributor.author (Authors) 陳庭鈞zh_TW
dc.contributor.author (Authors) Chen,Tin Jiunen_US
dc.creator (作者) 陳庭鈞zh_TW
dc.creator (作者) Chen,Tin Jiunen_US
dc.date (日期) 2005en_US
dc.date.accessioned 18-Sep-2009 14:30:17 (UTC+8)-
dc.date.available 18-Sep-2009 14:30:17 (UTC+8)-
dc.date.issued (上傳時間) 18-Sep-2009 14:30:17 (UTC+8)-
dc.identifier (Other Identifiers) G0093356031en_US
dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/35231-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 資訊管理研究所zh_TW
dc.description (描述) 93356031zh_TW
dc.description (描述) 94zh_TW
dc.description.abstract (摘要) 由於資訊科技的進步與網路的普及，企業得以收集與儲存大量的資料。使用資訊工具來協助資料處理、資訊擷取、以及產生知識已然變成企業的重要課題之一，所以如何良好運用資料探勘工具成為使用者關注的焦點。由於並非每一個使用者對於資料探勘的原理都有充分的了解，所以如何從探勘工具提供的功能中選用最佳的解決方案並不容易。如果對於探勘結果不滿意而需要調整軟體邏輯，與IT人員的協商溝通卻又曠日費時。

為了解決這個問題，本研究提出一個演算法選擇方法，藉由分析商業應用的內容，來自動對應到特定的資料探勘方法與演算法，讓選擇演算法的過程更為快速、更系統化，提升利用資料探勘工具解決商業問題的效率。		zh_TW
dc.description.abstract (摘要) Due to the information technology improvement and the growth of internet, companies are able to collect and to store huge amount of data. Using data mining technology to aid the data processing, information retrieval and knowledge generation process has become one of the critical missions to enterprise, so how to use data mining tools properly is users’ concern. Since not every user completely understand the theory of data mining, choosing the best solution from the functions which data mining tools provides is not easy. If user is not satisfied with the outcome of mining, communication with IT employees to adjust the software costs lots of time.

To solve this problem, a selection model of data mining algorithms is proposed. By analyzing the content of business application, user’s requirement will map to certain data mining category and algorithm. This method makes algorithm selection faster and reasonable to improve the efficiency of applying data mining tools to solve business problems.		en_US
dc.description.tableofcontents Table of Contents I
List of Tables III
List of Figures IV
Chapter 1 Introduction 1
1.1 Research Background 1
1.2 Research Objectives 1
1.3 Research Issues 2
1.4 Research Limitation 3
1.5 Research Flow 3
1.6 Organization of the Thesis 4
Chapter 2 Literature Review 5
2.1 Data Mining 5
2.1.1 Data Mining Definition 6
2.1.2 Data Mining Structure 8
2.1.3 Data Mining Method 9
2.1.4 Data Mining Modeling 11
2.2 Commercial Applications 12
2.3 Commercial Applications Related Works 14
2.4 Summary 23
Chapter 3 Research Method 24
3.1 Business Applications Analysis 24
3.2 Data Mining Algorithms Analysis 29
3.2.1 Association Rule Algorithms Analysis 32
3.2.2 Classification Rule Algorithms Analysis 33
3.2.3 Prediction Algorithms Analysis 34
3.2.4 Clustering Algorithms Analysis 34
3.3 Mapping Business Characteristic to Mining Concept 35
3.4 A Selection Model in the Application of Data Mining 37
3.4.1 Research Structure and an Example 37
3.4.2 Business Side 40
3.4.3 Mining Side 41
3.4.4 Selection Model 42
3.5 Summary 42
Chapter 4 Prototype Implementation 44
4.1 Prototype Platform and System Structure 44
4.2 Prototype System Design 44
4.2.1 Database Design 44
4.2.2 Function Design 46
4.3 Prototype System Implementation 46
Chapter 5 Research Experiment 52
5.1 Experimental Design 52
5.2 Test Cases 52
Chapter 6 Research Discussion 58
6.1 Managerial Findings 58
6.2 Technical Findings 59
Chapter 7 Conclusions and Future Research Directions 61
7.1 Conclusions 61
7.2 Future Research Directions 61
References 63		zh_TW
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dc.language.iso en_US-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0093356031en_US
dc.subject (關鍵詞) 資料探勘zh_TW
dc.subject (關鍵詞) 商業應用zh_TW
dc.subject (關鍵詞) 選擇方法zh_TW
dc.subject (關鍵詞) 資料探勘演算法zh_TW
dc.subject (關鍵詞) Data miningen_US
dc.subject (關鍵詞) Business applicationen_US
dc.subject (關鍵詞) Selection methoden_US
dc.subject (關鍵詞) Data mining algorithmen_US
dc.title (題名) 選擇商業應用資料探勘方法之框架zh_TW
dc.title (題名) A Framework for Selecting Data Mining Method in Business Applicationen_US
dc.type (資料類型) thesisen
dc.relation.reference (參考文獻) 1. Agrawal, R., Imielinski, T. and Swami, A. (1993, May). Mining association rules between sets of items in large databases. SIGMOD, Washington.zh_TW
dc.relation.reference (參考文獻) 2. Ahmed, S. R. (2004). Applications of Data Mining in Retail Business. Proceedings of the International Conference on Information Technology: Coding and Computing.zh_TW
dc.relation.reference (參考文獻) 3. Ahn, J. H., and Ezawa, K. J. (1997). Decision support for eeal-time telemarketing operations through bayesian network learning, Decision Support Systems. 21, 17-27zh_TW
dc.relation.reference (參考文獻) 4. Ahn, J. Y., Kim S. K. and Han, K. S. (2003). On the design concepts for CRM systems. Industrial Management and Data System. 103(5), 324-331.zh_TW
dc.relation.reference (參考文獻) 5. Anand, S. S., Patrick, A. R., Hughes, J. G., and Bell, D. A. (1998). A data mining methodology for cross-sales. Knowledge-Based Systems. 10, 449-461.zh_TW
dc.relation.reference (參考文獻) 6. Apte, C., Liu, Bing, Pednault, E. P. D. and Smyth P. (2002). Business applications of data mining. Communications of the ACM archive. 45(8), 49-53.zh_TW
dc.relation.reference (參考文獻) 7. Arshadi, N. and Jurisica, I. (2005). Data mining for case-based reasoning in high-dimensional biological domains. IEEE transactions on knowledge and data engineering. 17(8), 1127-1137.zh_TW
dc.relation.reference (參考文獻) 8. Bansal, K., Vadhavkar, S. and Gupta, A. (1998). Neural Networks Based Data Mining Applications for Medical Inventory Problems. International Journal of Agile Manufacturing, 1(2), 187-200.zh_TW
dc.relation.reference (參考文獻) 9. Berry, M. J. A. and Linoff G. (1997). Data mining techniques: for marketing, sales, and customer support. John Wiley & Sons Press.zh_TW
dc.relation.reference (參考文獻) 10. Bose, R. (2006). Intelligent technologies for managing fraud and identify Theft. Proceedings of the Third International Conference on Information Technology: New Generation. 446-451.zh_TW
dc.relation.reference (參考文獻) 11. Bose, R. (2002). Customer relationship management: key components for IT success. Industrial Management & Data Systems. 102(2), 89-97.zh_TW
dc.relation.reference (參考文獻) 12. Brachman, R. J., Khabasa, T., Kloesgen, W., Piatetsky-Shapiro, G., and Simoudis, E. (1996). Mining business database. Communication of ACM. 39(11), 42-48.zh_TW
dc.relation.reference (參考文獻) 13. Brause, R., Langsdorf, T. and Hepp, M. (1999). Neural Data Mining for Credit Card Fraud Detection. Proceedings of the 11th IEEE International Conference on Tools with Artificial Intelligence, Chicago. 103-106zh_TW
dc.relation.reference (參考文獻) 14. Chan, P. K., Fan, W., Prodromidis, A. L. and Stolfo, S.J. (1999). Distributed data mining in credit card fraud detection. Intelligent Systems and Their Applications. 14(6), 67-74zh_TW
dc.relation.reference (參考文獻) 15. Chaudhuri, S. and Dayal, U. (1997, March). An overview of data warehousing and OLAP technology. ACM SIGMOD Record. 26(1), 65 – 74.zh_TW
dc.relation.reference (參考文獻) 16. Chen, M. S., Han, J. and Yu, P. S. (1996). Data mining: an overview from database perspective. IEEE Transactions on Knowledge and Data Engineering. 8(6), 866-883zh_TW
dc.relation.reference (參考文獻) 17. Chen, R. S., Wu, R. C. and Chen J. Y. (2005) Data mining application in customer relationship management of credit card business. Proceedings of the 29th Annual International Computer Software and Applications Conference (COMPSAC’05). 2, 39-40.zh_TW
dc.relation.reference (參考文獻) 18. Chen, Y. and Hu, L. (2005). Study on data mining application in CRM system based on insurance trade. Proceedings of the 7th international conference on electronic commerce, Xi`an, China. 839-841.zh_TW
dc.relation.reference (參考文獻) 19. Davidson, A. and Simonetto, M., (2005). Pricing strategy and execution: an overlooked way to increase revenues and profits. STRATEGY & LEADERSHIP. 33(6), 25-33.zh_TW
dc.relation.reference (參考文獻) 20. Emili, T. A., (2004). Cost Efficiency and Product Mix Clusters across the Spanish Banking Industry. Review of Industrial Organization. 20, 163-181.zh_TW
dc.relation.reference (參考文獻) 21. Farquhar, J. D. (2004). Customer retention in retail, financial services: an employee perspective. The International Journal of Bank Marketing. 22(2), 86-99.zh_TW
dc.relation.reference (參考文獻) 22. Fayyad, U., Piatetsky-Shapiro, G. and Smyth, P. (1996). The KDD process for extracting useful knowledge from volumes of data. COMMUNICATIONS OF THE ACM. 39(11), 27-34zh_TW
dc.relation.reference (參考文獻) 23. Fayyad, U., Piatetsky-Shapiro, G., Smyth, P., and Uthurusamy, R. (1996). Advances in knowledge discovery and data dining. Cambridge: AAAI/MIT Press.zh_TW
dc.relation.reference (參考文獻) 24. Ferdousi, Z. and Maeda, A. (2006). Unsupervised outlier detection in time series data. Proceedings of the 22nd International Conference on Data Engineering Workshops (ICDEW`06). 51-56.zh_TW
dc.relation.reference (參考文獻) 25. Fish, K. E. and Segall, R. S. (2004) A visual analysis of learning rule effects and variable importance for neural networks in data mining operations. Kybernetes. 33(7), 1127-1142zh_TW
dc.relation.reference (參考文獻) 26. Forcht, K. A. and Cochran, K. (1999). Using data mining and datawarehousing techniques. Industrial Management & Data Systems. 99(5), 18-19.zh_TW
dc.relation.reference (參考文獻) 27. Gardner, M. and Bieker, J. (2000). Data mining solves tough semiconductor manufacturing problems. Conference on Knowledge Discovery in Data, Boston. ACM Press, 376 – 383.zh_TW
dc.relation.reference (參考文獻) 28. Gargano, M. L. and Raggad, B. G. (1999). Data mining – a powerful information creating tool. OCLC Systems & Services. 15(2), 81-90.zh_TW
dc.relation.reference (參考文獻) 29. Gavrilov, M., Anguelov, D., Indyk, P. and Motwani, R. (2000). Mining The Stock Market: Which Measure Is Best ?. Conference on Knowledge Discovery in Data, Boston, MA USA. 487-496.zh_TW
dc.relation.reference (參考文獻) 30. Goebel, M. and Gruenwald, L. (1999). A survey of data mining and knowledge discovery software tools. ACM SIGKDD Explorations Newsletter. 1(1), 20-33.zh_TW
dc.relation.reference (參考文獻) 31. Han, J. and Kamber, M. (2001). Data mining: concepts and techniques. Academic Press.zh_TW
dc.relation.reference (參考文獻) 32. Lee, S. J. and Siau, K. (2001). A review of data mining techniques. Industrial Management & Data Systems. 101(1), 41-46.zh_TW
dc.relation.reference (參考文獻) 33. Lee, T. S., Chiu C. C., Chou, Y. C. and Lu, C. J. (2006). Mining the customer credit using classification and regression tree and multivariate adaptive regression splines. Computational Statistics & Data Analysis. 50, 1113-1130.zh_TW
dc.relation.reference (參考文獻) 34. Lejeune, M. A. P. M. (2001). Measuring the impact of data mining on churn management. Internet Research: Electronic Networking Applications and Policy. 11(5), 375-387.zh_TW
dc.relation.reference (參考文獻) 35. Lim, T. S., Loh, W. Y., and Shih, Y. S. (2000). A Comparison of Prediction Accuracy, Complexity, and Training Time of Thirty-Three Old and New Classification Algorithms. Machine Learning. 40, 203-228.zh_TW
dc.relation.reference (參考文獻) 36. Lu, H. J., Feng, L., and Han, J. (2000). Beyond intratransaction association analysis- mining multidimensional intertransaction association rules. ACM Transactions of Information Systems. 18(4), 423-454.zh_TW
dc.relation.reference (參考文獻) 37. Lu, H. J., Han, J., and Feng, L. (1998). Stock movement prediction and N-dimensional inter-transaction association rules. ACM SIGMOD Workshop on Research Issues on Data Mining and Knowledge Discovery, Seattle. 12.1-12.7.zh_TW
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