Agrawal, R. & Shafer, J.C. (1996). Parallel mining of association rules, IEEE Transactions on Knowledge and Data Engineering, 8(6): 962–969.
Agrawal, R. & Srikant, R. (1994). Fast algorithms for mining association rules in large databases [A].Proc. of the 20th Int’l Conf on Very Large Data Bases [C]. Santiago: Morgan Kaufmann, 478-499.
Akhondzadeh-Noughabi, L. & Albadvi, A. & Aghdasi, M. (2014). Mining customer dynamics in designing customer segmentation using data mining techniques. Quarterly Journal of Information technology management, 6(1): 1-30.
Azar, A., Sangi, M., Izadkhah, M-M. & Anvari, A. (2015). Synergy management model of the holding by fuzzy approach, the role of information technology in its implementation. Quarterly Journal of Information technology management, 7(1): 1-22. (in Persian)
Azizi, SH., Abadi, V.H. & Balaghi Inanlou, M. (2014). Segmentation of Internet Banking Users Based on Expectations: A Data Mining Approach. Quarterly Journal of Information technology management, 6(3): 419-434. (in Persian)
Bezdek, J.C. (1981). Pattern Recognition with Fuzzy Objective Function Algorithms. Kluwer Academic Publishers Norwell, MA, USA.
Cakir, O. & Aras, M.E. (2012). A recommendation engine by using association rules. Procedia – social and Behavioral Sciences, 62(24): 452 – 456.
Chen, C., Hong, T. & Tseng, V. (2009). An improved approach to find membership functions and multiple minimum supports in fuzzy data mining. Expert Systems with Applications, 36(6): 10016–10024.
Dunham, M.H. (2002). Data Mining: Introductory and Advanced Topics. Prentice Hall PTR Upper Saddle River, NJ, USA.
Gottwald, S. (2006). Universes of Fuzzy Sets and Axiomatizations of Fuzzy Set Theory. Studia Logica,82(2): 211-244.
Han, J., Cheng, H., Xin, D. & Yan, X. (2007). Frequent pattern mining: current status and future Directions. Data Mining and Knowledge Discovery, 15(1): 55-88.
Hu, Y. & Chen, Y. (2006). Mining association rules with multiple minimum supports: a new mining algorithm and a support tuning mechanism. Decision Support Systems, 42(1): 1 – 24.
Hu, Y., Wu, F. & Liao, Y. (2013). An efficient tree-based algorithm for mining sequential patterns with multiple minimum supports. The Journal of Systems and Software, 86(5): 1224- 1238.
Huang, T. (2013). Discovery of fuzzy quantitative sequential patterns with multiple minimum supports and adjustable membership functions. Information Sciences, 222(10): 126-146.
Jalilmanesh, A. & Homaiounvala, A. (2011). Organizational Knowledge Mapping Based on Library Information System. IADIS Collaborative Technologies, Rome (Italy), 20-26.
Jang, J., Sun, C. & Mizutani, E. (1997). Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence. Paperback.
Lee, Y., Hong, T. & Wang, T. (2008). Multi-level fuzzy mining with multiple minimum supports. Expert Systems with Applications, 34(1): 459–468.
Lei, Z. & Ren-Hou, L. (2007). An Algorithm for Mining Fuzzy Association Rules Based on Immune Principles. Proceedings of the 7th IEEE International Conference on Bioinformatics and Bioengineering, BIBE. Boston, MA.
Liu, B. (2007). Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data, Springer.
Pea, J., Qualite, L. & Tille, Y. (2007). Systematic smpeling is a minimum support design. Computational statistics & Data Analysis, 51(12): 5591-5602.
Radfar, R., Nezafati, N. & Yousefi Asli, S. (2014). Classification of Internet banking customers using data mining algorithms. Quarterly Journal of Information technology management, 6(1): 71–90. (in Persian)
Shihab, A.I. & Burger, P. (1998). The Analysis of Cardiac Velocity MR Images Using Fuzzy Clustering. Proceeding of SPIE Medical Imaging Physiology and Function from Multidimensional Images, 3337(14): 176–183.
Tseng, M. & Lin, W. (2007). Efficient mining of generalized association rules with non-uniform minimum support. Data & Knowledge Engineering, 62(1): 41-64.
Lotfizadeh, A. (1965). Fuzzy sets. Information and Control
, 8 (3): 338-353.