Aghimien, D., Aigbavboa, C., & Oke, A. (2019). A review of the application of data mining for sustainable construction in Nigeria. Energy Procedia, 158, 3240–3245. https://doi.org/10.1016/j.egypro.2019.01.996

Alizadeh Zoeram, A., & Karimi Mazidi, A. R. (2018). New Approach for Customer Clustering by Integrating the LRFM Model and Fuzzy Inference System. Iranian Journal of Management Studies, 11(2), 351–378.

Anshari, M., Almunawar, M. N., Lim, S. A., & Al-Mudimigh, A. (2018). Customer relationship management and big data enabled: Personalization & customization of services. Applied Computing and Informatics. https://doi.org/10.1016/j.aci.2018.05.004

Barson, R. J., Foster, G., Struck, T., Ratchev, S., Pawar, K., Weber, F., & Wunram, M. (2000). Inter-and intra-organisational barriers to sharing knowledge in the extended supply-chain. Proceedings of the EBusiness and EWork, 18–20.

Bose, I., & Chen, X. (2015). Detecting the migration of mobile service customers using fuzzy clustering. Information & Management, 52(2), 227–238.

Burns, C., Sacks, G., & Gold, L. (2008). Longitudinal study of Consumer Price Index (CPI) trends in core and non‐core foods in Australia. Australian and New Zealand Journal of Public Health, 32(5), 450–453.

Buttle, F. (2009). Customer Relationship Management: Concepts and Technologies. Elsevier, Oxford.

Carrasco, R. A., Blasco, M. F., & Herrera-Viedma, E. (2015). A 2-tuple fuzzy linguistic RFM model and its implementation. Procedia Computer Science, 55, 1340–1347. https://doi.org/10.1016/j.procs.2015.07.118

Casidy, R. (2012). Discovering consumer personality clusters in prestige sensitivity and fashion consciousness context. Journal of International Consumer Marketing, 24(4), 291–299.

Chance, W. A. (1966). A note on the origins of index numbers. The Review of Economics and Statistics, 108–110.

Chang, H., & Tsay, S. (2004). Integrating od SOM and K-mean in data mining clustering: An empirical study of CRM and profitability evaluation. Journal of Information Management, 11, 161–203.

Chemchem, A., & Drias, H. (2015). From data mining to knowledge mining: Application to intelligent agents. Expert Systems with Applications, 42(3), 1436–1445.

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 PART 1), 4176–4184. https://doi.org/10.1016/j.eswa.2008.04.003

Christy, A. J., Umamakeswari, A., Priyatharsini, L., & Neyaa, A. (2018). RFM ranking – An effective approach to customer segmentation. Journal of King Saud University - Computer and Information Sciences. https://doi.org/10.1016/j.jksuci.2018.09.004

Dolca, I., & Nicolov, M. (2013). Analysis of Relationship between Net Wage and Consumer Price Index. Procedia Economics and Finance, 6, 738–747.

Faezy razi, F., & Shadloo, N. (2017). A Hybrid Grey based Two Steps Clustering and Firefly Algorithm for Portfolio Selection. Journal of Optimization in Industrial Engineering, 10(22), 49–59.

Garson, G. D. (2009). Cluster analysis: statnotes. Retrieved from http://faculty.chass.ncsu.edu/garson/pa765/statnote.htm

Ghalenooie, M. B., & Sarvestani, H. K. (2016). Evaluating Human Factors in Customer Relationship Management Case Study: Private Banks of Shiraz City. Procedia Economics and Finance, 36, 363–373. https://doi.org/10.1016/S2212-5671(16)30048-X

Greenberg, P. (2010). CRM at the Speed of Light: Social CRM Strategies, Tools, and Techniques. McGraw-Hill.

Gupta, G. P. (2018). Improved Cuckoo Search-based Clustering Protocol for Wireless Sensor Networks. Procedia Computer Science, 125, 234–240.

Gupta, S., & Zeithaml, V. (2006). Customer metrics and their impact on financial performance. Marketing Science, 25(6), 718–739.

Haislip, J. Z., & Richardson, V. J. (2017). The effect of Customer Relationship Management systems on firm performance. International Journal of Accounting Information Systems, 27, 16–29. https://doi.org/10.1016/j.accinf.2017.09.003

Harwati, A. M., & Karunia, A. (2017). Utilization of Social Media for Consumer Behavior Clustering using Text Mining Method. Journal of Engineering and Applied Sciences, 12(3 SI), 6406–6411.

https://doi.org/10.3923/jeasci.2017.6406.6411

Hu, Y.-H., Huang, T. C.-K., & Kao, Y.-H. (2013). Knowledge discovery of weighted RFM sequential patterns from customer sequence databases. Journal of Systems and Software, 86(3), 779–788.

Imani, A., & Abbasi, M. (2017). Customers Clustering Based on RFM Model by Using Fuzzy C-means Algorithm (Case Study: Zahedan City Refah Chain Store). Public Management Researches, 10(37), 251–276.

https://doi.org/10.22111/JMR.2017.3686

Kochetov, V. (2018). Overview of different approaches to solving problems of Data Mining. Procedia Computer Science, 123(123), 234–239. https://doi.org/10.1016/j.procs.2018.01.036

Li, G., & Sun, L. (2018). Characterizing Heterogeneity in Drivers’ Merging Maneuvers Using Two-Step Cluster Analysis. Journal of Advanced Transportation, 2018, 1–15. https://doi.org/10.1155/2018/5604375

Li, Y., Huang, J., & Song, T. (2019). Examining business value of customer relationship management systems: IT usage and two-stage model perspectives. Information & Management, 56(3), 392–402.

Merceron, A., & Yacef, K. (2004). Clustering students to help evaluate learning. IFIP World Computer Congress, TC 3, 31–42. Springer.

Mohammadian, M., & Makhani, I. (2016). RFM-Based customer segmentation as an elaborative analytical tool for enriching the creation of sales and trade marketing strategies. International Academic Journal of Accounting and Financial Management, 3(6), 21–35.

Mohammadzadeh, M., Hoseini, Z. Z., & Derafshi, H. (2017). A data mining approach for modeling churn behavior via RFM model in specialized clinics Case study: A public sector hospital in Tehran. Procedia Computer Science, 120, 23–30. https://doi.org/10.1016/j.procs.2017.11.206

Mozaffari A., Bonyadi A., A. F. (2010). Clustering and discovering the damage patterns of the third party insurance customers by using data mining techniques. The 10th International Conference on Industrial Engineering. Elsevier.

Ngai, E. W. T., Xiu, L., & Chau, D. C. K. (2009). Application of data mining techniques in customer relationship management: A literature review and classification. Expert Systems with Applications, 36(2), 2592–2602.

Peker, S., Kocyigit, A., & Eren, P. E. (2017). LRFMP model for customer segmentation in the grocery retail industry: A case study. Marketing Intelligence & Planning, 35(4), 544–559.

Preece, C., Chong, H. Y., Golizadeh, H., & Rogers, J. (2015). A review of customer relationship (CRM) implications: benefits and challenges in construction organizations. International Journal of Civil Engineering, 13(3), 362–371.

Rajeh, S. M., Koudehi, F. A., Seyedhosseini, S. M., & Farazmand, R. (2014). A model for customer segmentation based on loyalty using data mining approach and fuzzy concept in Iranian Bank. International Journal of Business and Behavioral Sciences, 4(9), 118–136.

Rémy, N. M., Martial, T. T., & Clémentin, T. D. (2018). The prediction of good physicians for prospective diagnosis using data mining. Informatics in Medicine Unlocked, 12, 120–127. https://doi.org/10.1016/j.imu.2018.07.005

Safari, F., Safari, N., & Montazer, G. A. (2016). Customer lifetime value determination based on RFM model. Marketing Intelligence & Planning, 34(4), 446–461.

Şchiopu, D. (2010). Applying TwoStep cluster analysis for identifying bank customers’ profile. Buletinul, 62, 66–75.

Soltani, Z., Zareie, B., Milani, F. S., & Navimipour, N. J. (2018). The impact of the customer relationship management on the organization performance. The Journal of High Technology Management Research, 29(2), 237–246.

Song, M., Zhao, X., E, H., & Ou, Z. (2017). Statistics-based CRM approach via time series segmenting RFM on large scale data. Knowledge-Based Systems, 132, 21–29. https://doi.org/10.1016/j.knosys.2017.05.027

Talón-Ballestero, P., González-Serrano, L., Soguero-Ruiz, C., Muñoz-Romero, S., & Rojo-Álvarez, J. L. (2018). Using big data from Customer Relationship Management information systems to determine the client profile in the hotel sector. Tourism Management, 68, 187–197. https://doi.org/10.1016/j.tourman.2018.03.017

Tanaka, T., Hamaguchi, T., Saigo, T., & Tsuda, K. (2017). Classifying and Understanding Prospective Customers via Heterogeneity of Supermarket Stores. Procedia Computer Science, 112, 956–964. https://doi.org/10.1016/j.procs.2017.08.133

Tekień, A., Gutkowska, K., Żakowska-Biemans, S., Jóźwik, A., & Krotki, M. (2018). Using cluster analysis and choice-based conjoint in research on consumers preferences towards animal origin food products. Theoretical review, results and recommendations. Animal Science Papers and Reports, 36(2), 171–184.

Tripathi, S., Bhardwaj, A., & Poovammal, E. (2018). Approaches to Clustering in Customer Segmentation. In International Journal of Engineering & Technology (Vol. 7). https://doi.org/10.14419/ijet.v7i3.12.16505

Triznova, M., Maťova, H., Dvoracek, J., & Sadek, S. (2015). Customer Relationship Management Based on Employees and Corporate Culture. Procedia Economics and Finance, 26, 953–959. https://doi.org/10.1016/s2212-5671(15)00914-4

Wang, Y., Assogba, K., Liu, Y., Ma, X., Xu, M., & Wang, Y. (2018). Two-echelon location-routing optimization with time windows based on customer clustering. Expert Systems with Applications, 104, 244–260. https://doi.org/10.1016/j.eswa.2018.03.018

Wang, Y., Ma, X., Lao, Y., & Wang, Y. (2014). A fuzzy-based customer clustering approach with hierarchical structure for logistics network optimization. Expert Systems with Applications, 41(2), 521–534.

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.

Xiao, J., Wang, M., Tian, L., & Zhen, Z. (2018). The measurement of China’s consumer market development based on CPI data. Physica A: Statistical Mechanics and Its Applications, 490, 664–680. https://doi.org/10.1016/j.physa.2017.08.135

Zerbino, P., Aloini, D., Dulmin, R., & Mininno, V. (2018). Big Data-enabled customer relationship management: A holistic approach. Information Processing & Management, 54(5), 818–846.

Zhang, T., Ramakrishnan, R., & Livny, M. (1996). BIRCH: an efficient data clustering method for very large databases. ACM Sigmod Record, 25(2), 103–114. ACM.

Chou, Tung-Hsiang and Shu-Chen Chang. "The RFM Model Analysis for VIP Customer: A Case Study of Golf Clothing Brand." IJKM vol.18, no.1 2022: pp.1-18. http://doi.org/10.4018/IJKM.290025

Hu, Dingding, Zhou, Kaile, Li, Fangyi  & Ma, Dawei. (2022). Electric vehicle user classification and value discovery based on charging big data. Energy,249,