آذر، عادل؛ احمدی، پرویز و محمد وحید سبط. (1389). طراحی مدل انتخاب نیروی انسانی با رویکرد داده‌کاوی (مورد: استخدام داوطلبان آزمون‌های ورودی یک بانک تجاری در ایران). نشریه مدیریت فناوری اطلاعات، (2)4: 3-22، 986-1001.
بیگدلی، زاهد و بهروز فرهادیان. (1381). نظر مراجعان کتابخانه های عمومی خوزستان درباره تناسب مجموعه ها با نیازهای اطلاعاتی آنان. علوم اجتماعی و انسانی دانشگاه شیراز، 36: 204-218.
رزمی، جعفر و آرش قنبری. (1388). ارائه مدلی نوین جهت محاسبه ارزش دوره عمر مشتری، نشریه مدیریت فناوری و اطلاعات، 1(1): 35-50.
عمادخراسانی، نسرین دخت. (1382). خدمات عمومی کتابخانه و روش‌های آن. تهران: نشر کتابدار، چاپ سوم.
غضنفری، مهدی (1391). داده کاوی و کشف دانش. تهران: دانشگاه علم و صنعت ایران.
کفاش پور، آذر؛ توکلی، احمد و علی علیزاده زوارم. (1391). بخش‌بندی مشتریان بر اساس ارزش دوره عمر آن‌ها با استفاده از داده‌کاوی بر مبنای مدل آر. اف. ام. (RFM). پژوهش‌های مدیریت عمومی، 5(15)، 63-84.
مهرگان، محمد رضا. (1383). پژوهش عملیاتی پیشرفته. تهران: انتشارات کتاب دانشگاهی، چاپ اول.
Barry, M. (2003). Data Mining. 9th Indian. Delhi: Pearson University
Bin, D., Peiji, S., and Dan, Z. (2008). Data mining for needy student identify based on improved RFM model: A case study of university, International conference on information management, innovation management and industrial engineering, New York.
Buttle, F. (2004). Customer Relationship Management: Concepts and Tools. Elsevier Butterworth Heinemann.
Cannavo, S. (2003). The Power of Logic in Everyday Life. Mumbai:Magna Publishing Co.Ltd.
Chang, P. C., and Lai, C. Y. (2005). A hybrid system combining self-organizing maps with case-based reasoning in wholesaler’s new-release book forecasting. Expert Systems with Applications, 29, 183–192.
Cheng, C. H., and Chen, Y. S. (2009). Classifying the segmentation of customer value via RFM model and RS theory. Expert systems with applications, 36, 4176-4184.
Guha, S., Rastogi, R., and Shim, K. (1998). Cure: an efficient clustering algorithm for large databases: Proceedings of the ACM SIGMOD conference, 73-84.
Han, J., and Kamber, M. (2006). Data mining: concepts and techniques, San Morgan, Francisco Kaufman.
Hsieh, N. C. (2004). An integrated data mining and behavioral scoring model for analyzing bank customers. Expert systems with applications,27, 623-633.
Hu, W., and 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. (1994). Strategic database marketing, Chicago: Probus publishing.
Kantardzic, M (2003). Data Mining: Concepts, Mdels, Methods, and algorithms, wiley – inter science.
Keiningham, T. L., Aksoy. A., and Bejou, D. (2006). Approches to measurement and management of customer value. Journal of relationship marketing. 5 (2), 37-54.
Khajvand, M., and Tarokh, M. J. (2011). Estimating customer future value of different customers segments based on adapted RFM model in retail banking context. Procedia Computer Science, 3, 1327-1332.
Kim, Y. S., and Sohn, S. Y. (2004). Managing loan customers using misclassificationpatterns of credit scoring model. Expert Systems With Applications, (26), 567-573.
Kohonen, T. (2001). Self- organizing Maps, Springer Series in Information sciences, Springer, Berlin, New York, Volume 30.
Kohonen, T., and Makisara, k. (1984). Phonotypic maps –insightful representation of phonological features for speech recognition, Los Alamitos, CA. IEEE Computer Soc. Press, 182 -185.
Kreidler, M (2008). Guide to auto insurance, Washington State Office of the Insurance Commissioner, viwed 2010/4/24 <www.insurance.wa.gov>
Lee, T. S., and Chen, I. F. (2005). A two – stage hybrid credit scoring model using systems with applications, (28), 743-725.
Li, D. C., Dai, W. L., and Tseng, E. T. (2011). A twostage clustering mehod to analyze customer characteristics to build discriminative customer management: A case of textile manufacturing business. Expert Systems With Applications, 14, 1-6.
Namvar, M., Gholamian, M. R., and KhakAbi, S (2010). A Two Phase Clustering Method For Intelligent Customer Segmentation. International Conference on Intelligent Systems, Modelling and Simulation, IEEE 2010.
Ngai, E. W. T., Xiu, L., and Chau, D. C. K. (2009). Application of data mining techniques in customer relationship management: A literature review and classification. Expert Systems With Applications, 36, 2592-2602.
Niyagas, W., Srivihok, A., and Kitisin, S (2006). Clustering e-banking customer using dataminind and marketing segmentation. Ectitransactions On Compter And Information Technology, 2(1).
Parker, V. N. (1990). Determining Priorities for Compuerisation in Libraries. Annals of Library Science and Documentation, 37(3), 110-116.
Patkar, V. N. (2005). Data mining applications in library and academic institutions. Information Studies Journal of Science Direct, 11(3): 145-156.
Penn, B. S. (2005). Using self-organizing maps to visualize high-dimensional data. Computers & Geosciences, (31), 531–544.
Saggaf, M. M., Toksoz, M. N., and Marhoon, M. I. (2003). Seismic facies classification and dentification by competitive neural networks: Geophysics, (44), 1041-1063.
Seo, S., and Obermayer, K (2004). Self-organizing maps and clustering methods for matrix data. Neural Networks, 17, 1211–1229.
Seyed Hosseini, M., Maleki, A., and Gholamian, M. R (2010). Cluster analysis using data mining approach to develop CRM methodology to assess the customer loyalty. Expert Systems With Applications, 37, 5259-5264.
Sohrabi, B., and 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., and Lo, C. F (2009). Applying RFM model and K-means method in customer value analysis of an outfitter, International Conference on Concurrent Engineering, New York.
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