在顧客行銷中，一個重要的環節，即是如何將顧客正確分類，一旦能從過去資料中，尋找出一套良好的分類機制，我們便可對不同的客群，做一對一的行銷。因此，分類的準確度對管理者而言，是一個重要的課題，不正確的分類資訊，將可能導致嚴重的決策錯誤。

資料探勘在資料分類方面，已有多種應用工具。然而，一般的分類器（如決策樹）是以少數法則來分類大量的資料，在多元複雜的資料當中，錯誤分類是在所難免的。相對而言，關聯法則可以找出滿足特定門檻值的情況下之所有法則，藉由門檻值的調整，可獲得更多潛在有用的分類法則。

關聯式分類器之準確度與其分類器法則優先順序有關，因此，本研究使用基因演算法的特點，改良關聯式分類器，以達到更準確的分類目的。另外，本研究以機器學習常用之公開測試資料做驗證，來比較其與決策樹兩者之準確度。依據結果顯示，使用基因演算法確實可以有效提升分類器之準確度。

none

中文部份

Roiger, R. J. & Geatz, M. W. 著／曾新穆、李建億 譯，「資料探勘」，台灣培生教育出版股份有限公司，民國92年。

英文部份

Abramson, D. & Abela, J., “A Parallel Genetic Algorithm for Solving the School Timetabling Problem”, Proceedings of the 15th Australian Computer Science Conference (ACSC-15) , Vol. 14, pp. 1-11, 1992.

Agrawal, R., Imielinski, T. & Swami, A., “Mining Association Rules between Sets of Items in Large Databases”, Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, pp. 207-216, Washington, D.C., 1993.

Agrawal, R. & Srijant, R., “Fast Algorithms for Mining Association Rules”, Proceedings of the 20th VLDB Conference, pp. 487-499, Santiago, Chile, 1994.

Ali, K., Manganaris, S. & Srikant, R., “Partial Classification using Association Rules”, Proceedings of the 3rd International Conference on Knowledge Discovery in Databases and Data Mining, pp. 115-118, Newport Beach, California, 1997.

Berry, M. & Linoff, G., “Data Mining Techniques: For Marketing, Sales, and Customer Support”, John Wiley & Sons, 1997.

Berry, M. & Linoff, G., “Mastering Data Mining: The Art and Science of Customer Relationship Management”, John Wiley & Sons, 1999.

Bloemer, J. M. M., Brijs, T., Swinnen, G., & Vanhoof, K., “Comparing Complete and Partial Classification for Identifying Customers at Risk”, International Journal of Research in Marketing, Vol. 20 (2), pp. 117-131, 2003.

Bodon, F. & Rόnyai, L., “TRIE: An Alternative Data Structure for Data Mining Algorithms”, Mathematical and Computer Modelling, Vol. 38, pp. 739-751, 2003.

Davis, L., “Handbook on Genetic Algorithms”, Van Nostrand Reinhold, 1991.

Fayyad, U. & Stolorz, P., “Data Mining and KDD: Promise and Challenges”, Future Generation Computer Systems, Vol. 13, No. 2-3, pp. 99-115, 1997.

Goldberg, D. E., “Genetic Algorithms in Search, Optimisation and Machine Learning”, Addison Wesley, 1989.

Han, J. & Kamber, M., “Data Mining: Concepts and Techniques”, Morgan Kaufmann, 2001.

Han, J., Pei, J. & Yin, Y., “Mining Frequent Patterns without Candidate Generation”, Proceedings of the ACM SIGMOD Conference on Management of Data, pp. 1-12, 2000.

Hand, D., Mannila, H. & Smyth, P., “Principles of Data Mining”, MIT Press, 2001.

Holland, J. H., “Adaptation in Natural and Artificial Systems”, The University of Michigan Press, 1975.

Kantardzic, M., “Data Mining: Concepts, Models, Methods, and Algorithms”, John Wiley & Sons, 2002.

Koonce, D. A. & Tsai, S. C., “Using Data Mining to Find Patterns in Genetic Algorithm Solutions to a Job Shop Schedule”, Computers & Industrial Engineering, Vol. 38, pp. 361-374, 2000.

Li, W., Han, J. & Pei, J., “CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules”, In IEEE International Conference on Data Mining, pp. 369-376, San Jose, CA, 2001.

Li, J., Shen, H. & Topor, R., “Mining the Optimal Class Association Rule Set”, Knowledge-Based Systems, Vol. 15, Issue 7, pp. 399-405, 2002.

Liu, B., Hsu, W. & Ma, Y., “Integrating Classification and Association Rule Mining”, Proceedings of 1998 International Conference on Knowledge Discovery and Data Mining, pp. 80-86, New York, NY, Aug 1998.

Mitchell, M., “An Introduction to Genetic Algorithms”, MIT Press, 1996.

Negnevitsky, M., “Artificial Intelligence: A Guide to Intelligent Systems”, Addison Wesley, 2001.

Quinlan, J. R., “Induction of Decision Trees”, Machine Learning, Vol. 1, No. 1, pp. 81-106, 1986.

Quinlan, J. R., “C4.5: Programs for Machine Learning”, Morgan Kaufmann, 1993.

Rud, O. P., “Data Mining Cookbook: Modeling Data for Marketing, Risk and Customer Relationship Management”, John Wiley & Sons, 2000.

Syswerda, G., “Schedule Optimization Using Genetic Algorithms. In Lawrence Davis, editor, Handbook of Genetic Algorithms”, Van Nostrand Reingold, 1991.

網站部份

Blake, C. L. & Merz, C. J., “UCI Repository of machine learning databases”, http://www.ics.uci.edu/~mlearn/MLRepository.html, Irvine, CA: University of California, Department of Information and Computer Science, 1998.

Microsoft, “Microsoft® Data Mining Boot Camp 2002商業智慧研討會－解析資料採擷趨勢與成功建置經驗，提高客戶滿意度及商業價值”, http://www.microsoft.com/taiwan/press/2002/0620.htm, June 2002.