隨著產業市場環境國際化與自由化，國內企業面臨的競爭也越來越激烈，在如此激烈的環境中企業要如何獲取最大利益是相當重要的。企業為因應國際化的趨勢，提高產品品質及降低生產成本，已是最基本的要求，若要能夠脫穎而出勢必需要透過客戶關係管理找出主要客戶在哪裡。因此，建置一套能準確的篩選出主要目標客戶選擇模型的確有其必要性。
在不同的產業中客戶篩選的標準都不同，不外乎是透過訂貨量來做區分，但在藥品市場中考量的因素更多，所以透過資料探勘技術，從所有因素當中找出主要的決定因素，將可提升客戶分級的準確性。因此，本研究將利用資料探勘技術中的決策樹（Decision Tree）、支持向量機（Support Vector Machines, SVM）、關聯法則與群集分析現有資料，找出分級規則，並提出整合性之解釋。

With the globalization and liberalization of the industrial market environment, the competition faced by domestic companies is also becoming more and more intense. It is very important for companies to obtain maximum benefits in such a fierce environment. In order to meet the trend of internationalization, enterprises must improve product quality and reduce production costs. This is the most basic requirement. To be able to stand out from the crowd, it is necessary to find out where the major customers are through customer relationship management. Therefore, it is indeed necessary to establish a set of accurate selection of the main target customer selection model.
In different industries, the screening criteria for customers are different. It is nothing more than distinguishing through order quantity. However, there are more factors considered in the pharmaceutical market. Therefore, through data mining technology, the main determinants are determined from all factors , and thay will improve the accuracy of customer ratings. Therefore, this study uses the decision tree, Support Vector Machines （SVM）, association rules and cluster analysis to analyze the existing data, find the classification rules, and propose integrated interpretation.

1.CRM研究小組（譯）（2004）。戀愛高手CRM - 就是要擄獲顧客的心。（原著者藤田憲一）桃園:先鋒企管。
2.WEKA 3-5-5 Explorer 使用者指南。
3.尹相志（2009）。SQL Server 2008 Data Mining資料採礦。台北:悅知文化。
4.行政院經濟部工業局（2017）。2017 生技產業白皮書。台北市:行政院經濟部工業局。
5.行政院衛生福利部食藥署（2012）。食品藥物管理局說明藥品管理與品質監控。台北市:行政院衛生福利部食藥署。
6.林盈源（2003）。決策樹在資料庫行銷決策之應用。成功大學工業管理科學系碩士在職專班學位論文。
7.吳怡靜（2012）。讓顧客升級，創造最賺錢的顧客。天下雜誌247期。
8.袁梅宇（2016）。王者歸來：WEKA機器學習與大數據聖經［第3版］.台北:佳魁資訊。
9.陳樹衡、郭子文、棗厥庸（2007）。以決策樹之迴歸樹建構住宅價格模型—台灣地區之實證分析。住宅學報，第十六卷，第一期，頁1-20。
10.陳美純（2009）。客戶關係管理的教學與應用。叡揚e論壇/56期。
11.陳有星（2004）。利用供應鏈管理提高企業的競爭優勢，現代管理科學，第1期，頁58。
12.曾瑞智（2013）。應用資料探勘技術建構整合型目標客戶選擇模式。大同大學資訊經營學系碩士學位論文。
13.蔡崇煌、翁紹仁、周駿安、吳信宏、洪偉展（2017）。運用C4.5決策樹分析失眠症狀。台灣衛誌，第三十六卷，第五期，頁449-460。
14.劉文良（2016）。顧客關係管理應用與認證：顧客關係管理助理管理師認證指定教材［第二版］。台北:碁峰資訊。
15.顏秀雯（2003）。主要客戶管理。Career 就業情報，2003年9月4日。
16.Berry, M. J., & Linoff, G.（1997）. Data mining techniques: For marketing, sales, and customer support. John Willey and Sons, New York.
17.Berry, M., & Linoff, G.（2004）. G Data Mining Techniques: for marketing, sales, and customer relationship management , USA.
18.Cortes, C., & Vapnik, V.（1995）. Support-vector networks, Machine learning, Vol.20, No.3, pp.273-297.
19.Chang, C. C., & Lin, C. J.（2011）. LIBSVM: a library for support vector machines. ACM transactions on intelligent systems and technology （TIST）, Vol.2, No.3, pp.27.
20.Deza, M. M., & Deza, E.（2009）. Encyclopedia of distances. In Encyclopedia of Distances , pp. 1-583. Springer, Berlin, Heidelberg.
21.Fayyad, U. M., Piatetsky-Shapiro, G., & Smyth, P.（1996, August）. Knowledge Discovery and Data Mining: Towards a Unifying Framework. In KDD, Vol. 96, pp. 82-88.
22.Frawley, W. J., Piatetsky-Shapiro, G., & Matheus, C. J. （1992）. Knowledge discovery in databases: An overview. AI magazine, Vol. 13, No.3, pp.57.
23.Kalakota, R.（2001）. Robinson., M.,（2001）, E-business 2.0: roadmap for success. MA: Addison-Wesley.
24.Kalakota, R., & Robinson, M.（1999）. e-Business. Roadmap for Success: Addison Wesley.
25.MacQueen, J.（1967）. Some methods for classification and analysis of multivariate observations. In Proceedings of the fifth Berkeley symposium on mathematical statistics and probability , Vol. 1, No. 14, pp. 281-297.
26.McSherry, D.（1999）. Strategic induction of decision trees. Knowledge-Based Systems, Vol.12, No5, PP.269-275.
27.Peppers, D., Rogers, M., & Dorf, B.（1999）. Don’t put customer relationship on hold.Sales and Marketing Management, Vol.25, No.1, pp.26-28.
28.Quinlan, J. R.（2014）. C4. 5: programs for machine learning. Elsevier.
29.Viera, A. J., & Garrett, J. M.（2005）. Understanding interobserver agreement: the kappa statistic. Fam Med, Vol.37, No.5, pp.360-363.
30.Vapnik, V.（2014）. Invited Speaker. IPMU Information Processing and Management of Uncertainty in Knowledge-Based Systems.
31.Yada, K.（2011）. String analysis technique for shopping path in a supermarket. Journal of Intelligent Information Systems, Vol.36, No.3, pp.385-402.
32.Zeithaml, V. A., Lemon, K. N., & Rust, R. T.（2001）. Driving customer equity: How customer lifetime value is reshaping corporate strategy. Simon and Schuster.