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Application of Data Mining Classification in Employee Performance Prediction

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
John M. Kirimi, Christopher A. Moturi

John M Kirimi and Christopher A Moturi. Application of Data Mining Classification in Employee Performance Prediction. International Journal of Computer Applications 146(7):28-35, July 2016. BibTeX

	author = {John M. Kirimi and Christopher A. Moturi},
	title = {Application of Data Mining Classification in Employee Performance Prediction},
	journal = {International Journal of Computer Applications},
	issue_date = {July 2016},
	volume = {146},
	number = {7},
	month = {Jul},
	year = {2016},
	issn = {0975-8887},
	pages = {28-35},
	numpages = {8},
	url = {},
	doi = {10.5120/ijca2016910883},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


In emerging knowledge economies such as Kenya, organizations rely heavily on their human capital to build value. Consequently, performance management at the individual employee level is essential and the business case for implementing a system to measure and improve employee performance is strong. Data Mining can be used for knowledge discovery of interest in Human Resources Management (HRM). We used the Data Mining classification technique for the extraction of knowledge significant for predicting employee performance using previous appraisal records a public management development institute in Kenya. The Cross Industry Standard Process for Data Mining (CRISP-DM) was adopted for predictive analysis. Decision tree was the main Data Mining tool used to build the classification model, where several classification rules were generated. To validate the developed model, a prototype was constructed and the data collected from the institute’s Human Resource Department was used. Results show that employee performance was highly affected by experience, age, academic qualification, professional training, gender, marital status and previous performance appraisal scores. This paper proposes a prediction model for employee performance forecasting that enables the human resource professionals to refocus on human capability criteria and thereby enhance the performance appraisal process of its human capital.


  1. Al-Radaideh, Q.A., Al-Nagi, E., (2012). Using Data Mining Techniques to Build a Classification Model for Predicting Employees Performance, International Journal of Advanced Computer Science and Applications, 3(2), pp 144 – 151
  2. Ancheta, R.A, Cabauatan, R.J.M., Lorena, B.T.T., Rabago, W., (2012). Predicting faculty development trainings and performance using rule-based classification algorithm, Asian Journal of Computer Science and Information Technology 2: 7, pp 203 – 209.
  3. hein, C.F., Chen, L.F., (2008). Data Mining to improve personnel selection and enhance human capital: A case study in high technology industry, Expert Systems with Applications, 34(1), pp 280–290
  4. Delavari, N., Phon-Amnuaisuk S., (2008). Data Mining Application in Higher Learning Institutions, Informatics in Education, 7(1), pp. 31–54
  5. Hamidah J., Abdul R.H., Zulaiha A.O., (2009). Knowledge Discovery Techniques for Talent Forecasting in Human Resource Applications, World Academy of Science, Engineering and Technology, 3
  6. Jantan, H., Hamdan, A. R., Othman, Z. A. (2011). Towards applying Data Mining techniques for talent management. International Conference on Computer Engineering and Applications IPCSIT Vol. 2.
  7. Jantan, H., Hamdan, A. R., & Othman, Z. A. (2010). Human talent prediction in HRM using C4. 5 classification algorithm, International Journal on Computer Science and Engineering, 2(08-2010), pp 2526-2534
  8. Jantan, H., Hamdan, A. R., Othman, Z. A. (2009). Knowledge discovery techniques for talent forecasting in human resource application. World Academy of Science, Engineering and Technology, Penang, Malaysia, pp 803-811
  9. Jayanthi R., D.P. Goyal, S.I Ahson, (2008). Data Mining techniques for better decisions in human resource management systems, International Journal of Business Information Systems, 3(5), pp 464 – 481
  10. Kotsiantis, S.B., (2007). Supervised machine learning: a review of classification techniques, Informatica, 31, pp 249-268.
  11. Kurgan, L.A., Musilek, P. (2006). A survey of knowledge discovery and Data Mining Models, The Knowledge Engineering Review, 21(1), pp 1 - 24
  12. Mishra, P., Padhy, N., Panigrahi, R. (2013). The survey of Data Mining applications and feature scope. Asian Journal of Computer Science & Information Technology, 2(4), pp 67 – 77
  13. Phyu, T.N., (2009). Survey of classification techniques in data mining, Proceedings of the International Multi Conference Of Engineers And Computer Scientists, IMECS 2009, Vol 1
  14. Ranjan, J., Goyal,D.P., S I Ahson, S.I., (2008). Data mining techniques for better decisions in human resource management systems, International Journal of Business Information Systems 3(5) pp 464 – 481
  15. Sarda, V., Sakaria, P., Sindhu Nair, S., (2014). Relevance Ranking Algorithm for Job Portals, International Journal of Current Engineering and Technology, 4(5), pp 3157 – 3160
  16. Strohmeier, S., Piazza, F., (2013). Domain driven Data Mining in human resource management: A review of current research, Expert Systems with Applications, 40(7), pp 2410–2420
  17. Valle, M.A., Varas, S., Ruz, G.A., (2012). Job performance prediction in a call center using a Naive Bayes classifier, Expert Systems with Applications, 39(11), pp 9939–9945
  18. Witten, I.,H., Frank, E., (2005). Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann Publishers, Elsevier Inc.
  19. Zhao, X.(2008). A Study of Performance Evaluation of HRM: Based on Data Mining, FITME 2008, International Seminar on Future Information Technology and Management Engineering


Employee Performance Prediction, Data Mining, Data Mining Classification, C4.5 (J4.8) Algorithm.