@article{(Open Science Index):https://publications.waset.org/pdf/9997660,
	  title     = {ATM Service Analysis Using Predictive Data Mining},
	  author    = {S. Madhavi and  S. Abirami and  C. Bharathi and  B. Ekambaram and  T. Krishna Sankar and  A. Nattudurai and  N. Vijayarangan},
	  country	= {},
	  institution	= {},
	  abstract     = {The high utilization rate of Automated Teller Machine (ATM) has inevitably caused the phenomena of waiting for a long time in the queue. This in turn has increased the out of stock situations. The ATM utilization helps to determine the usage level and states the necessity of the ATM based on the utilization of the ATM system. The time in which the ATM used more frequently (peak time) and based on the predicted solution the necessary actions are taken by the bank management. The analysis can be done by using the concept of Data Mining and the major part are analyzed based on the predictive data mining. The results are predicted from the historical data (past data) and track the relevant solution which is required. Weka tool is used for the analysis of data based on predictive data mining.
},
	    journal   = {International Journal of Computer and Information Engineering},
	  volume    = {8},
	  number    = {2},
	  year      = {2014},
	  pages     = {311 - 315},
	  ee        = {https://publications.waset.org/pdf/9997660},
	  url   	= {https://publications.waset.org/vol/86},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 86, 2014},
	}