Anbari, A., Nadali, A. & Eslami Nosrat Abadi, H. (2010). Comparing data mining algorithms for predicting auto insurance policy holders` risk: 4th Iran data mining conference, Tehran: Sharif University of Technology, December 1st, 1-10. (in Persian)
Choobdar, S. (2008). Designing a framework for the future customers of vehicle collision coverage based on data mining) Master`s Thesis), Tarbiat Modarres University, Tehran. (in Persian)
Fathnejad, F. & Izadparast, M. (2011). Presenting a framework for predicting damage level of vehicle collision coverage policy holders with using data mining approach. Insurance World Updates, 156(3), 15-32. (in Persian)
Ghanty, P., Paul, S. & Pal, N. (2009). NEUROSVM: An architecture to reduce the effect of the choice of kernel on the performance of SVM. Journal of Machine Learning Research, 10(3), 591-622.
Gharekhani, M. & Abolghasemi, M. (2011). Data mining applications in insurance industry. Insurance World Updates, 158(5), 5-21. (in Persian)
Guo, L. (2003). Applying DM in property/casualty insurance. University of Central Florida: CAS Committee on Management Data and Information, Florida.
Haji Heydari, N., Khale`, S. & Farahi, A. (2011). Classifying the risk of vehicle collision coverage policy holders with using data mining algorithms. Iranian Journal of Insurance Research, 26(4), 107-129. (in Persian)
Hanafizadeh, P. & Rastkhiz Paydar, N. (2011). A model for risk-based clustering of vehicle collision coverage customers with using data mining technique. Iranian Journal of Insurance Research, 26(2), 55-81. (in Persian)
Iran Insurance Research Center. (2011). Insurance Research Center Annual Statistical Report. Tehran. (in Persian)
Kiavarz Moghaddam, H. & Wang, X. (2014). Vehicle accident severity rules mining using fuzzy granular decision tree. Rough Sets and Current Trends in Computing, 36(85), 280-287.
Mansouri, M. & Kargar, M.J. (2014). Analysis and monitoring of the traffic suburban road accidents using data mining techniques: A case study of Isfahan province in Iran. The Open Transportation Journal, 8(1), 39-49.
Mohammadi, Sh. & Alizadeh, S. (2014). Analyzing the problems of Ayandeh bank`s branches across the country with using data mining technique. Journal of Information Technology Management, 6(2), 333-350. (in Persian)
Momeni, M. (2006). New Approaches in Operations Research (1st ed), University of Tehran: Faculty of Management, Tehran. (in Persian)
Newstead, S. & D`Elia, A. (2007). An investigation into the relationship between vehicle color and crash risk. Monash University Accident Research Center: Report No.263, Melbourne.
Ngai, E., Xiu, L. & Chau, D. (2009). Application of Data Mining techniques in customer relationship management: a Literature Review and Classification. Expert Systems with Application, 36(2), 592-602.
Nisbet, R., Elder, J. & Miner, G. (2009). Handbook of statistical analysis and Data Mining applications, Burlington: Academic Press.
Parnitzke, T. (2008). A Discussion of Risk Assessment Methods for the German Automobile Insurance Industry (Doctoral Dissertation), University of St.Gallen: Institute of Insurance Economics, St. Gallen.
Radfar, R., Nezafati, N. & Yusefi Asl, S. (2014). Clustering customers of e-banking with using data mining algorithms. Journal of Information Technology Management, 6(1), 71-90. (in Persian)
Raut, R. & Nathe, A. (2015). Comparative study of commercial data mining tools. International Journal of Electronics, Communication & Self Computing Science and Engineering, 8, 128-132.
Vosugh, M., Taghavifard, M.T. & Alborzi, M. (2014). Detecting fraud in credit cards with using artificial neural networks. Journal of Information Technology Management, 6(4), 721-746. (in Persian)
Yeo, A., Smith, K., Willis, R. & Brooks, M. (2001). Modeling the effect of premium changes on motor insurance customer retention rates using neural networks. Computational Science. 2074, 390-399.