{"title":"ATM Service Analysis Using Predictive Data Mining","authors":"S. Madhavi, S. Abirami, C. Bharathi, B. Ekambaram, T. Krishna Sankar, A. Nattudurai, N. Vijayarangan","volume":86,"journal":"International Journal of Computer and Information Engineering","pagesStart":311,"pagesEnd":316,"ISSN":"1307-6892","URL":"https:\/\/publications.waset.org\/pdf\/9997660","abstract":"<p>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.<\/p>\r\n","references":"[1]\tArvind Sharma, P.C. Gupta; \u2018Predicting the Number of Blood Donors through Their Age and Blood Group by using Data Mining Tool\u2019, International Journal of Communication and Computer Technologies, Volume 01 \u2013 No.6, Issue: 02, September 2012.\r\n[2]\tOracle Data Mining Concepts, 10g Release 1 (10.1), Part Number B10698-01, \u2018Predictive Data Mining Models\u2018, file:\/\/\/H:\/project\/ PAPERS\/New%20Mining%20Papers\/Predictive%20Data%20Mining%20Models.htm\r\n[3]\tDr. Wenjia Wang; \u2018Data Mining and Statistics within the Health Services\u2019, 19\/02\/2010\r\n[4]\tZdravko Markov, Ingrid Russell; \u2018An Introduction to the WEKA Data Mining System\u2019, Central Connecticut State University\r\n[5]\tBy Wayne W. Eckerson; \u2018Predictive Analytics Extending the Value of Your Data Warehousing Investment\u2019, TDWI Best Practices Report, First quarter 2007\r\n[6]\tMehta Neel B, \u2018Predictive Data Mining and Discovering Hidden Values of Data Warehouse\u2019, ARPN Journal of Systems and Software, Volume 1 No. 1, APRIL 2011\r\n[7]\tS Abdulsalam Sulaiman Olaniyi, Adewole, Kayode S, Jimoh, R. G; \u2018Stock Trend Prediction Using Regression Analysis \u2013A Data Mining Approach\u2019, ARPN Journal of Systems and Software, Volume 1 No. 4, July 2011\r\n[8]\tGodswill Chukwugozie Nsofor; \u2018A Comparative Analysis Of Predictive Data-Mining Techniques\u2019, August, 2006\r\n[9]\tGhulam Mujtaba Shaikh and Tariq Mahmood; Mining and Adaptivity in Automated Teller Machines, 2012\r\n[10]\tVasumathi, Dhanavanthan, 2010, \"Application of Simulation Technique in Queuing Model for ATM Facility\u201d, Volume 1, No 3\r\n[11]\tHyun-Chul Kim, Shaoning Pang, Hong-Mo Je, Daijin Kim, and Sung-Yang Bang; Support Vector Machine Ensemble with Bagging, 2002\r\n[12]\tBurges, C; \u2019A Tutorial on Support Vector Machines for Pattern Recognition. Data Mining and Knowledge Discovery\u2019. 2(2) (1998) 121\u2013167 397, 400.\r\n","publisher":"World Academy of Science, Engineering and Technology","index":"Open Science Index 86, 2014"}