اسدپور، نوید؛ و شاهرودی، کامبیز. (1392). جداسازی و رتبه‌بندی مشتریان خوش‌حساب بانک رفاه با رویکرد داده‌کاوی. دومین همایش ملی بررسی راهکارهای ارتقاء مباحث مدیریت، حسابداری و مهندسی صنایع در سازمان‌ها.
ایزدپرست، سید محمود؛ و فتح‌نژاد، فرامرز. (1391). ارائه چارچوب برای پیش‌بینی سطح خسارت مشتریان بیمه بدنه اتومبیل با استفاده از راهکار داده‌کاوی، تازه‌های جهان بیمه، 156، 32-15.
خالصی، نرگس؛ و شکوهی، امیرحسین. (1389). ارائه روشی جدید برای اعتبارسنجی مشتریان بانکی با استفاده از تکنیک‌های داده‌کاوی. چهارمین کنفرانس داده‌کاوی ایران. دانشگاه صنعتی شریف 1389. تهران.
رزمی، جعفر؛ و قنبری، آرش. (1388). ارائه مدلی نوین جهت محاسبه ارزش دوره عمر مشتری،نشریهمدیریتفناوریواطلاعات، 1(1)، 35-50.
زین‌العابدینی، سیده فاطمه. مهدوی، مهرگان؛ و خان بابایی، محمد. به‌کارگیری تکنیک‌های داده‌کاوی و بازاریابی در بخش‌بندی و تحلیل رفتار مشتریان بانکداری الکترونیکی. کنفرانس ملی فناوری اطلاعات و جهاد اقتصادی.
کفاش‌پور، آذر. توکلی، احمد؛ و علیزاده زوارم، علی. (1391). بخش‌بندی مشتریان بر اساس ارزش دوره عمر آن‌ها با استفاده از داده‌کاوی بر مبنای مدل آر.اف.ام. (RFM). پژوهش‌های مدیریت عمومی. (15)، 63-84.
Alvandi, M. Fazli, S. & Abdoli, F. S. (2012). K-Mean clustering method for analysis customer lifetime value with LRFM relationship model in banking services. International Research Journal of Applied and Basic Sciences, 3(11), 2294-2302.
Balaji, S. & Srivatsa, S. K. (2012). Decision Tree induction based classification for mining Life Insurance Data bases. International Journal of Computer Science and Information Technology & Security (IJCSITS), ISSN, 2249-9555.
Buttle, F. (2009). Customer Relationship Management. Concepts and Tools, Butterworthe.
Cheng, C. H. & Chen, Y. S. (2009). Classifying the segmentation of customer value via RFM model and RS theory. Expert systems with applications, 36(3), 4176-4184.
Demuth, H. & Beale, M. (1993). Neural network toolbox for use with MATLAB.
Farajian, M. A. & Mohammadi, S. (2010). Mining the banking customer behavior using clustering and association rules methods. International Journal of Industrial Engineering, 21(4).
Han, J. Kamber, M. & Pei, J. (2011). Data mining: concepts and techniques: concepts and techniques. Elsevier.
Hanafizadeh, P. & Paydar, N. R. (2013). A data mining model for risk assessment and customer segmentation in the insurance industry.International Journal of Strategic Decision Sciences, 4(1), 52-78.
Hosseini, S. M. S. Maleki, A. & Gholamian, M. R. (2010). Cluster analysis using data mining approach to develop CRM methodology to assess the customer loyalty. Expert Systems with Applications, 37(7), 5259-5264.
Hu, W. & Jing, Z. (2008). Study of segmentation for auto services companies based on RFM model, [online], <http:// www. pucsp.br/icim/ingles/downloads/pdf_procceeding_2008/66. pdf >.
Hughes, A. M. (2005). Strategic database marketing. McGraw-Hill Pub. Co.
Jayalakshmi, T. & Santhakumaran, A. (2011). Statistical normalization and back propagation for classification. International Journal of Computer Theory and Engineering, 3(1), 1793-8201.
Keiningham, T. L. Aksoy, L. & Bejou, D. (2006). Approaches to the measurement and management of customer value. Journal of Relationship Marketing, 5(2-3), 37-54.
Khajvand, M. & Tarokh, M. J. (2011). Estimating customer future value of different customer segments based on adapted RFM model in retail banking context. Procedia Computer Science, 3, 1327-1332.
Li, D. C. Dai, W. L. & Tseng, W. T. (2011). A two-stage clustering method to analyze customer characteristics to build discriminative customer management: A case of textile manufacturing business. Expert Systems with Applications, 38(6), 7186-7191.
Li, W. Wu, X. Sun, Y. & Zhang, Q. (2010, December). Credit card customer segmentation and target marketing based on data mining. In Computational Intelligence and Security (CIS), 2010 International Conference on (pp. 73-76). IEEE.
Li, X. & Chan, C. W. (2010). Application of an enhanced decision tree learning approach for prediction of petroleum production. Engineering Applications of Artificial Intelligence, 23(1), 102-109.
Li, X. Chan, C. W. & Nguyen, H. H. (2013). Application of the Neural Decision Tree approach for prediction of petroleum production. Journal of Petroleum Science and Engineering, 104, 11-16.
Liang, Y. H. (2010). Integration of data mining technologies to analyze customer value for the automotive maintenance industry. Expert Systems with Applications, 37(12), 7489-7496.
Liu, D. R. & Shih, Y. Y. (2005). Hybrid approaches to product recommendation based on customer lifetime value and purchase preferences. Journal of Systems and Software, 77(2), 181-191.
Namvar, M. Gholamian, M. R. & KhakAbi, S. (2010, January). A two phase clustering method for intelligent customer segmentation. In Intelligent Systems, Modelling and Simulation (ISMS), 2010 International Conference on (pp. 215-219). IEEE.
Rezaeinia, S. M. Keramati, A. & Albadvi, A. (2012). An integrated AHP-RFM method to banking customer segmentation. International Journal of Electronic Customer Relationship Management, 6(2), 153-168.
Soeini, R. A. & Rodpysh, K. V. (2012). Evaluations of Data Mining Methods in Order to Provide the Optimum Method for Customer Churn Prediction: Case Study Insurance Industry. In 2012 International Conference on Information and Computer Applications (ICICA 2012) IPCSI (Vol. 24).
Sohrabi, B. & Khanlari, A. (2007). Customer lifetime value (CLV) measurement based on RFM model. Iranian Accounting & Auditing Review, 14(47), 7-20.
Wu, H. H. Chang, E. C. & Lo, C. F. (2009). Applying RFM model and K-means method in customer value analysis of an outfitter. In Global Perspective for Competitive Enterprise, Economy and Ecology (pp. 665-672). Springer London.
Wu, K. S. Wang, F. K. & Shyng, J. Y. (2013). Applied data mining techniques in insurance company: A comparative study of rough sets and decision tree.African Journal of Business Management, 7(24), 2309-2318.
Yen, S. J. & Lee, Y. S. (2011). A neural network approach to discover attribute dependency for improving the performance of classification. Expert Systems with Applications, 38(10), 12328-12338.
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