[1] Bose, I. and Chen, X., 2014. Detecting temporal changes in customer behavior. 2014 International Electrical Engineering Congress (iEECON), pp.3-6. https://doi.org/10.1109/iEECON.2014.6925923.
[2] Böttcher, M., Spott, M., Nauck, D. and Kruse, R., 2009. Mining changing customer segments in dynamic markets. Expert Systems with Applications, 36(1), pp.155-164. https://doi.org/10.1016/j.eswa.2007.09.006.
[3] Su, C., Chen, Y., and Sha, D.Y., 2006. Linking innovative product development with customer knowledge: a datamining approach. Technovation, 26, pp.784-795. https://doi.org/10.1016/j.technovation.2005.05.005.
[4] Ngai, E., Xiu, L. and Chau, D.C., 2009. Application of data mining techniques in customer relationship management: A literature review and classification. Expert Systems with Applications, 36(2), pp.2592-2602. https://doi.org/10.1016/j.eswa.2008.02.021.
[5] Salehi, M., Salari, M., 2017. Comparing data mining and fuzzy logic techniques to identify behavior of customers, Modern Research in Decision Making, 2(3), pp. 173-192. [In Persian].
[6] Prowost, F. and Fawcett, T., 2013. Data Science for Business. O’reilly Media, Inc.
[7] Akhondzadeh-Noughabi, E. and Albadvi, A., 2015. Mining the dominate patterns of customer shifts between segments by using top-k and distinguishing sequential rules. Management Decision, 53(9), pp.1976-2003. https://doi.org/10.1108/MD-09-2014-0551.
[8] Mach-Król, M. and Hadasik, B., 2021. On a Certain Research Gap in Big Data Mining for Customer Insights. Applied Sciences, 11(15), pp.6993. https://doi.org/10.3390/app11156993.
[9] Mosaddegh, A., Albadvi, A., Sepehri, M. and Teimourpour, B., 2021. Dynamic of customer segments: A predictor of customer lifetime value. Expert Systems with Applications, 172. https://doi.org/10.1016/j.eswa.2021.114606.
[10] Song, M., Zhao, X., E, H. and Ou, Z., 2017. Statistics-based CRM approach via time series segmenting RFM on large scale data. Knowledge-Based Systems, 132, pp.21-29. https://doi.org/10.1016/j.knosys.2017.05.027.
[11] Seret, A., Vanden Broucke, S.K., Baesens, B. and Vanthienen, J., 2014. A dynamic understanding of customer behavior processes based on clustering and sequence mining. Expert Systems with Applications, 41(10), pp.4648-4657. https://doi.org/10.1016/j.eswa.2014.01.022.
[12] Yu, L., Zhang, Z. and Shen j., 2017. Dynamic customer preference analysis for product portfolio identification using sequential pattern mining. Industrial Management & Data Systems, 117(2). https://doi.org/10.1108/IMDS-12-2015-0496.
[13] Parvatiyar, A. and Sheth, J.N., 2001. Customer relationship management: Emerging practice, process, and discipline. Journal of Economic & Social Research, 3(2).
[14] Kincaid, J.W., 2003. Customer relationship management: getting it right. Prentice Hall Professional.
[15] Swift, R.S., 2001. Accelerating customer relationships: Using CRM and relationship technologies. Prentice Hall Professional.
[16] Bashardoust, O., Asgharizadeh, E., Afshar Kazemi, M. A., 2022. Customers Clustring Analysis Based on WRFM Model Using Non-Supervisory Data Mining Approach (Case study of hygienic and cosmetic products), Modern Research in Decision Making, 7(1), pp. 198-223. [In Persian].
[17] Vakil, S., teymoor nejad, K., motadel, M., moammadi, M., 2022. Presenting a Conceptual framework of Customer Relationship Management in Electronic Banking with Emphasis on Using Business Intelligence Tools (Case Study: Sepah Bank and Merged Banks), Management Research in Iran, 26(1), pp. 246-271. [In Persian].
[18] Gür Ali, Ö. and Aritürk, U., 2014. Dynamic churn prediction framework with more effective use of rare event data: The case of private banking. Expert Systems with Applications, 41, pp.7889-7903. https://doi.org/10.1016/j.eswa.2014.06.018.
[19] Crespo, F. and Weber, R., 2005. A methodology for dynamic data mining based on fuzzy clustering. Fuzzy Sets and Systems, 150(2), pp.267-284. https://doi.org/10.1016/j.fss.2004.03.028.
[20] Abbasimehr, H. and Sheikh Baghery, F., 2022. A novel time series clustering method with fine-tuned support vector regression for customer behavior analysis. Expert Systems with Applications, 204. https://doi.org/10.1016/j.eswa.2022.117584.
[21] Sivaguru, M., 2022. Dynamic customer segmentation: a case study using the modified dynamic fuzzy c-means clustering algorithm. Granul. Comput, 8, pp.345-360. https://doi.org/10.1007/s41066-022-00335-0.
[22] Norouzi, A., Teymourpour, B., Chubdar, S., Sepehri, M., 2021. Developing a model for discovering the causes of customer churn from banking services via hybrid approach of data mining and survey, Management Research in Iran, 15(4), pp. 97-125. [In Persian].
[23] Alizadeh, M., Sadrian Zadeh, D., Moshiri, B. and Montazeri, A., 2023. Development of a Customer Churn Model for Banking Industry Based On Hard and Soft Data Fusion. IEEE Access, 11. https://doi.org/10.1109/ACCESS.2023.3257352.
[24] Safinejad, F., Akhond Zadeh Noughabi, E. and H. Far, B., 2018. A Fuzzy Dynamic Model for Customer Churn Prediction in Retail Banking Industry. Applications of Data Management and Analysis, pp.85–101. https://doi.org/10.1007/978-3-319-95810-1_7.
[25] Abdullah, F. and Jalil, Z., 2022. A Novel FCM and DT based Segmentation and Profiling Approach for Customer Relationship Management. 2022 2nd International Conference on Artificial Intelligence (ICAI), pp.112-117. https://doi.org/10.1109/ICAI55435.2022.9773772.
[26] Viviani, J., Komura, A. and Suzuki, K., 2021. Integrating dynamic segmentation and portfolio theories for better performance. Journal of Strategic Marketing, 31(1), pp.140-153. https://doi.org/10.1080/0965254X.2021.1881148.
[27] Schröer, C., Kruse, F. and Gómez, J.M., 2021. A Systematic Literature Review on Applying CRISP-DM Process Model. Procedia Computer
