IJRCS – Volume 5 Issue 2 Paper 1


Author’s Name : B Kannan | R Kumerasan

Volume 05 Issue 02  Year 2018  ISSN No:  2349-3828  Page no: 1- 3



Evaluating employee’s performance and undesirable behavior in professional environments is an important task. Employee’s performance Professional details & performance is based upon various factors like personal details or demographic, Social, Performance details etc. The data mining techniques are more helpful in classifying professional database and help us in evaluating the performance and undesirable behavior of a employee. Data mining techniques are widely useful in professional data mining for analysis of employee data. In professional area data mining different data mining techniques like classification, clustering, association rule mining. The main goal of data mining process is to extract information from large amount of data and to translate raw data into meaning full information. These databases contain hidden information for improvement of employees’ performance. The performance in higher professional in India is a turning point in the performances for all employees. This performance is influenced by many factors, therefore it is essential to develop predictive data mining model for employees’ performance so as to identify the difference between high learners and slow learners employee.


Employee Performance, Dot Net, Data Mining


  1. Nat’l Research Council, Building a Workforce for the Information Economy, Nat’l Academies Press, 2001.
  2. C. Romero, S. Ventura, and E. Garca,“Data Mining in Course Management Systems: Moodle Case Study and Tutorial,” Computers & Professional, vol. 51, no. 1, 2008, pp. 368–384.
  3. L. Pappano, “The Year of the MOOC, ”The New York Times, 2 Nov. 2012;
  4. Z. Pardos et al., “Adapting Bayesian Knowledge Tracing to a Massive Open Online Course in edX,” Proc. 6th Int’l Conf. Professional Data Mining (EDM 13), 2013;
  5. A. Elbadrawy, R.S. Studham, and G. Karypis, “Collaborative Multi regression Models for Predicting Employees’ Performance in Course Activities,” Proc. 5th Int’l Conf. Learning Analytics and Knowledge (LAK 15), 2015, pp. 103–107.
  6. M. Sweeney, J. Lester, and H. Rangwala, “Next-Term Employee Grade Prediction,” Proc. IEEE Int’l Conf. Big Data (Big Data 15), 2015, pp. 970–975.
  7. A. Polyzou and G. Karypis, “Grade Prediction with Course and Employee Speci c Models,” to be published in Proc. 20th Pacic Asia Conf. Knowledge Discovery and Data Mining (PAKDD), 2016.
  8. S. Aud et al., The Condition of Professional 2013, NCES report no. 2013-037, Nat’l Center for Professional Statistics, US Department of Professional.