عنوان مقاله [English]
Abstract ATM terminals as the most used e-banking channels could be a threat for banks. Some years ago due to a few numbers of ATM, they were taken into account as a competitive advantage; despite most of the time they were out of order. But nowadays their breakdown is a potential threat for banks to lose their customers. On the other hand the amount of data generated by ATM’s maintenance system increases at an unprecedented rate. In this way data mining is introduced as a method for discovering knowledge from these amounts of data in order to predict their failure and disruption. This paper introduces architecture for collecting data generated from ATMs performance and classifying them by predicting their breakdowns. So a sample of 1039 ATM was selected and the needed data were collected. Data were processed by C&R tree and Chaid models in Clementine software. Due to the better result of C&R tree algorithm, it was selected for modeling. By analyzing the results, ATMs were classified into 3 groups and some suggestions were given to maintenance team for planning better
 Hemmati M., Shahhoseini M., Javidyar M. (2010) "Identifying and prioritizing effective factors in attracting in customer trough ATMs", Second Financial Services Marketing International Conference, Iran, Tehran.
 Saidi A. (2005) "Data mining and its application in higher education", Journal of Higher Education, 18(3): 1-9.
 Esmaili Rad E., Zeynal Hamedani R. (2009) "Application of data mining in improved maintenance and repairmen", Behboud, 9 (25): 11-16.
 Bastos P., Lopes I., Pires L. (2014) "Application of data mining in a maintenance system for failure Prediction", Journal of Safety, Reliability and Risk Analysis, 3(7): 933-940.
 Hasanzadeh A., Ghanbari M., Elahi S. (2012) "Classification mobile bank users by data mining approach: Comparing neural network and Bayes' theorem", Management Researches in Iran, 16 (2), pp: 57-71.
 Parto M., (2013) "Using problem solving tools and techniques for detecting wastes", Engineering and Technical Journal of Construction and Production, 52: 46-50.
 Eshraghnia R., Shanechi H., Rajabi-e-mashhadi H., (2005) "Scheduling Production departments in competitive environment by using genetic algorithm", 3rd national Conference on Maintenance, Iran, Tehran.
 Garcı´a F. P., Pedregal J. D., Roberts C. (2010) "Time series methods applied to failure prediction and detection reliability", Journal of Engineering and System Safety,12(4): 698-703.
 Ferreiro S., Sierra B., Irigoien A., Gorritxategi E. (2011) "Data mining for quality control: Burr detection in the drilling process", Journal of Computers & Industrial Engineering, 60: 801-810.
 Abbasi M., Samadi M., Safari M. A., Faraj R. (2013) "Introducing a method for conditional maintenance and repairmen by considering number and type of errors", 2nd Sird Regional Conference, Iran, Tehran.
 Pariazar M., Zaeri S., Shahrabi J., (2007) "Data mining application in maintenance and repairmen", First Conference on Data Mining, Iran, Tehran.
 Ahmadloo Y., (2009), "Applying data mining as competitive tool in banking", 3rd International Conference on Electronic Banking, Iran, Tehran.
 Jain A. K., Murty M. N., Flynn P. J, (1999) "Data clustering: A Review", International Journal of ACM Computing Surveys, 31(3): 299-322.
 Xiong T., Wang S., Mayers A., Monga E. (2013) "Personal bankruptcy prediction by mining credit card data", International journal of Expert Systems with Applications, 40(2): 665-676.
 Afsar A., Hoshdar Mahajoub R., Minaie B. (2014) "Customer credit clustering for presenting appropriate facilities", Management Researches in Iran, 17(4): 1-22.
 Chakrabarti S. et al (2009) "Data mining, know it all", United States, Morgan Kaufmann; Elsevier, eBook.
 Bashiri Mousavi A., Afsar A., Mahajoubifar A. (2014) "Analyzing customer value by using data mining and Analytical hierarchy process techniques", Management Researches in Iran, 19(1): 33-43.