[1] Mehta J. S., Gawande A. (2015) "A purpose of data mining in banking sector", International Journal of Advance Research in Computer Science and Management Studies, 3(3).
[2] Ramos S., Duarte J. M., Duarte F. J., Vale Z. (2015) "A data-mining-based methodology to support MV electricity customers’ characterization", Energy and Buildings, 91": 16-25.
[3] Chen C. C., Chen A. P. (2007)"Using data mining technology to provide a recommendation service in the digital library", The Electronic Library, 25(6): 711-724. doi: doi:10.1108/02640470710837137.
[4] Afsar A., Houshdar Mahjoub R., Minaie Bidgoli B. (2014) "Customer credit clustering for presenting appropriate facilities", Management Researches in Iran, 17(4):1-24.
[5] Mahdavi K., Horri M.S., (2015) "Designing a model for predicting the credit ranking of bank customers using meta-heuristic algorithm and multi-criteria hybrid neuro-fuzzy and ant colony (Case Study of Post Bank Branches of Tehran), Management Researches in Iran,
[6] Sajjadi K., Khatami-Firuzabadi M. A., Amiri M., Sadaghiani J. S. (2015) "A developing model for clustering and ranking bank customers", International Journal of Electronic Customer Relationship Management, 9(1): 73-86. doi: doi:10.1504/IJECRM.2015.070701.
[7] Ngai E., Hu Y., Wong Y., Chen Y., Sun X. (2011) "The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature", Decision Support Systems, 50(3): 559-569.
[8] Ramos S., Duarte J. M., Duarte F. J., Vale Z. (2015) "A data-mining-based methodology to support MV electricity customers’ characterization", Energy and Buildings, 91": 16-25.
[9] Q. He, (1999) "A review of clustering algorithms as applied in IR", Graduate School of Library and Information Science University of Illinois, at Urbana-Champaign.
[10] Jianying M., Yongjian F., Yanguang S. (2009) A neural networks-based clustering collaborative filtering algorithm in E-commerce recommendation system, Paper presented at the Web Information Systems and Mining, WISM 2009, International Conference.
[11] Das J., Mukherjee P., Majumder S., Gupta P. (2014), "Clustering-based recommender system using principles of voting theory", Paper Presented at the Contemporary Computing and Informatics (IC3I) Conference, Amity University, Noida, India.
[12] Asosheha A., Bagherpour S.,Yahyapour N. (2008) "Extended acceptance models for recommender system adaption, Case of retail and banking service in Iran", WSEAS Transactions on Business and Economics, 5(5):189-200.
[13] Zahra S., Ghazanfar M. A., Khalid A., Azam M. A., Naeem U., Prugel-Bennett A. (2015) "Novel centroid selection approaches for KMeans-clustering based recommender systems", Information Sciences, 320, 156-189. doi:
http://dx.doi.org/10.1016/j.ins.2015.03.062.
[14] Shani G., Gunawardana A. (2011) "Evaluating recommendation Systems", In F. Ricci, L. Rokach, B. Shapira, & P. B. Kantor (Eds.), Recommender Systems Handbook (pp. 257-297): Springer, US.
[15] Guo, G., et al. (2015). "Leveraging multiviews of trust and similarity to enhance clustering-based recommender systems." Knowledge-Based Systems 74: 14-27.
[16] Hsieh N.C. (2004) An integrated data mining and behavioral scoring model for analyzing bank customers. Expert Systems with Applications, 27(4): 623-633.
[17] Kim J.B., Song B. Y., Zhang Y. (2015) "Earnings performance of major customers and bank loan contracting with suppliers", Journal of Banking & Finance, 59:384-398. doi: http://dx.doi.org/10.1016/j.jbankfin.2015.06.020
[18] Anh N. D. (2015) Adaptive neuro-Fuzzy network for recommendation, International University HCMC, Vietnam.
[19] de Oña R., de Oña J. (2015) "Analysis of transit quality of service through segmentation and classification tree techniques", Transportmetrica A: Transport Science, 11(5): 365-387, doi: 10.1080/23249935.2014.1003111.
[20] Hsieh N.C. (2004) An integrated data mining and behavioral scoring model for analyzing bank customers. Expert Systems with Applications, 27(4): 623-633.
[21] Schetinin V., Jakaite L., Jakaitis J., Krzanowski W. (2013) "Bayesian decision trees for predicting survival of patients: A study on the US national trauma data bank", Computer Methods and Programs in Biomedicine, 111(3): 602-612. doi: http://dx.doi.org/10.1016/j.cmpb.2013.05.015
[22] Seret A.,Vanden Broucke S. K. L. M., Baesens B., Vanthienen J. (2014) "A dynamic understanding of customer behavior processes based on clustering and sequence mining", Expert Systems with Applications, 41(10), 4648-4657. doi: http://dx.doi.org/10.1016/j.eswa.2014.01.022.
[23] Yang X., Chen J., Hao P., Wang Y. J. (2015) Application of clustering for customer segmentation in private banking, Seventh International Conference on Digital Image Processing (ICDIP 2015), 96311Z (July 6, 2015); doi:10.1117/12.2197182.
[24] Islam M., Habib M. (2015) A data mining approach to predict prospective business sectors for lending in Retail banking using decision tree, arXiv preprint arXiv:1504.02018.