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Knowledge Discovery Techniques for Talent Forecasting in Human Resource Application

Authors: Hamidah Jantan, Abdul Razak Hamdan, Zulaiha Ali Othman


Human Resource (HR) applications can be used to provide fair and consistent decisions, and to improve the effectiveness of decision making processes. Besides that, among the challenge for HR professionals is to manage organization talents, especially to ensure the right person for the right job at the right time. For that reason, in this article, we attempt to describe the potential to implement one of the talent management tasks i.e. identifying existing talent by predicting their performance as one of HR application for talent management. This study suggests the potential HR system architecture for talent forecasting by using past experience knowledge known as Knowledge Discovery in Database (KDD) or Data Mining. This article consists of three main parts; the first part deals with the overview of HR applications, the prediction techniques and application, the general view of Data mining and the basic concept of talent management in HRM. The second part is to understand the use of Data Mining technique in order to solve one of the talent management tasks, and the third part is to propose the potential HR system architecture for talent forecasting.

Keywords: HR Application, Knowledge Discovery inDatabase (KDD), Talent Forecasting.

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[1] Ranjan, J., Data Mining Techniques for better decisions in Human Resource Management Systems. International Journal of Business Information Systems, 2008. 3(5): p. 464-481.
[2] DeNisi, A.S. and R.W. Griffin, Human Resource Management. 2005, New York: Houghton Mifflin Company.
[3] A TP Track Research Report Talent Management: A State of the Art. 2005, Tower Perrin HR Services.
[4] Stavrou-Costea, E., The challenges of human resource management towards organizational effectiveness A comparative study in Southern EU. Journal of European Industrial, 2005. 29(2): p. 112-134.
[5] DeCenZo, D.A. and S.P. Robbins, Fundamentals of Human Resource Management. 8th Ed. ed. 2005, New York: John Wiley & Son.Inc. .
[6] Okpara, J.O. and P. Wynn, Human resource management practices in a transition economy: Challenges and prospects. Management Research News, 2008. 31(1): p. 57-76.
[7] Hooper, R.S., et al., Use of an Expert System in a personnel selection process. Expert Systems and Applications, 1998. 14(4): p. 425-432.
[8] Hamidah, J., H. Abdul Razak, and A.O. Zulaiha. Potential Intelligent Techniques in Human Resource Decision Support System (HR DSS). in Proceedings 3rd International Symposium on Information Technology 2008. Kuala Lumpur: IEEE
[9] Martinsons, M.G., Knowledge-based systems leverage human resource management expertise. International Journal of Manpower, 1995. 16(2): p. 17-34.
[10] Chien, C.F. and L.F. Chen, Data mining to improve personnel selection and enhance human capital: A case study in high-technology industry. Expert Systems and Applications, 2008. 34(1): p. 380-290.
[11] Tai, W.S. and C.C. Hsu (2005) A Realistic Personnel Selection Tool Based on Fuzzy Data Mining Method. http://www.atlantispress. com/php/download_papaer?id=46 9/1/2008.
[12] Huang, L.C., et al. Applying fuzzy neural network in human resource selection system. in Proceeding NAFIPS '04. IEEE Annual Meeting of the Fuzzy information 2004. 2004.
[13] Huang, L.C., et al., A neural network modelling on human resource talent selection. International Journal of Human Resource Development and Management, 2001. 1(Number 2-4): p. 206-219.
[14] Quintero, A., D. Konare, and S. Pierre, Prototyping an Intelligent Decision Support System for improving urban infrastructures management. European Journal of Operational Research, 2005. 162(3): p. 654-672.
[15] Qian, Z., G.H. Huang, and C.W. Chan, Development of an intelligent decision support system for air pollution control at coal-fired power plants. Expert System with Applications, 2004. 26(3): p. 335-356.
[16] Viademonte, S. and F. Burstein, From Knowledge Discovery to computational Intelligent : A Framework for Intelligent Decision Support System. 2006, London: Springer London.
[17] Chen, K.K., et al., Constructing a Web-based Employee Training Expert System with Data Mining Approach, in Paper in The 9th IEEE International Conference on E-Commerce Technology and The 4th IEEE International Conference on Enterprise Computing, ECommerce and E-Services (CEC-EEE 2007). 2007.
[18] Mehrabad, M.S. and M.F. Brojeny, The development of an expert system for effective selection and appointment of the jobs applicants in human resource management. Computers & Industrial Engineering, 2007. 53(2): p. 306-312.
[19] Liao, S.-H., A knowledge-based architecture for implementing collaborative problem-solving methods in military e-training. Expert Systems and Applications, 2007. In Press(Corrected Proof).
[20] Tung, K.Y., et al., Mining the Generation Xer's job attitudes by artificial neural network and decision tree - empirical evidence in Taiwan. Expert Systems and Applications, 2005. 29(4): p. 783-794.
[21] Chien, C.F. and L.F. Chen, Using Rough Set Theory to Recruit and Retain High-Potential Talents for Semiconductor Manufacturing. IEEE Transactions on Semiconductor Manufacturing, 2007. 20(4): p. 528-541.
[22] Huang, M.J., Y.L. Tsou, and S.C. Lee, Integrating fuzzy data mining and fuzzy artificial neural networks for discovering implicit knowledge. Knowledge-Based Systems, 2006. 19(6): p. 396-403.
[23] Glenzer, C., A conceptual model of an interorganizational intelligent meeting-scheduler (IIMS). Strategic Information Systems, 2003. 12(1): p. 47-70.
[24] Bozbura, F.T., A. Beskese, and C. Kahraman, Prioritization of human capital measurement indicators using fuzzy AHP. Expert Systems and Applications, 2007. 32(4): p. 1100-1112.
[25] Haddawy, P. and N.T.N. Hien (2007) A decision support system for evaluating international student applications. peter_haddaway_and%20-hyuyen_thi_ngoc_hien.pdf 9/1/2008.
[26] Pardos, Z., et al. (2007) The effect of Model Granularity on Student Performance Prediction using Bayesian Networks.
[27] Sullivan, W.G. and W.W. Claycombe Technological Fundamentals of forecasting. 1977, Virginia: Reston Publishing Company, Inc.
[28] Tso, G.K.F. and K.K.W. Yau, Predicting electricity energy comsumption : A comparison of regression analysis, decision tree and nerural networks. Energy, 2007. 32: p. 1761 - 1768.
[29] Delen, D., G. Walker, and A. Kadam, Predicting breast cancer survivability: A comparison of three data mining methods. Artificial Intelligent in Medicine, 2005. 34(2): p. 113-127.
[30] Chang, L.Y. and W.C. Chen, Data mining of tree-based models to analyze freeway accident frequency. Journal of Safety Research 2005. 36(4): p. 365-375.
[31] Becerra-Fernandez, I., S.H. Zanakis, and S. Walczak, Knowledge discovery techniques for predicting country investmnet risk. Computers & Industrial Engineering, 2002. 43(4): p. 787-800.
[32] Enke, D. and S. Thawornwong, The use of data mining and neural networks for forecasting stock market returns. Expert Systems with Applications, 2005. 29(4): p. 927-940.
[33] Hong, T. and I. Han, Knowledge-based data mining of news information on the internet using cognitive maps and neural networks. Expert Systems with Applications, 2002. 23(1): p. 1-8.
[34] Liew, P.L., et al., Comparison of artificial neural networks with logistic regression in predicition of Gallbladder disease among obese patients. Digestive and Liver Disease, 2007. 39(4): p. 356-362.
[35] Lin, F.Y. and S. McClean, A data mining approach to the prediction of corporate failure. Knowledge-Based Systems, 2001. 14(3-4): p. 189-195.
[36] Cardoso, G. and F. Gomide, Newspaper demand prediction and replacement model based on fuzzy clustering and rules. An International Journal on Information Sciences, 2007. 177(21): p. 4799- 4809.
[37] Chang, F.J. and Y.T. Chang, Adaptive neuro-fuzzy inference system for prediction of water level in reservoir. Advances in Water Resources, 2006. 29(1): p. 1-10.
[38] Balaguer, E., et al., Predicting service request in support centers based on nonlinear dynamics, ARMA modeling and neural networks. Expert Systems with Applications, 2008. 34(1): p. 665-672.
[39] Kim, S.H. and H.J. Noh, Predictability of Interest Rates using data mining tools : A comparative analysis of Korea and the US. Expert Systems with Applications, 1997. 13(2): p. 85-95.
[40] Xue, Y. and D.E. Brown, Spatial analysis with preference specification of latent decision makers for criminal event prediction. Decision Support Systems, 2006. 41(3): p. 560-573.
[41] Yavas, G., et al., A data mining approach for location prediction in mobile environments. Data & Knowledge Engineering, 2005. 54(2): p. 121-146.
[42] Kim, K.-j., Artificial Neural Networks with evolutionary instance selection for financial forecasting. Expert Systems with Applications, 2006. 30(3): p. 519-526.
[43] Chou, S.M., et al., Mining the breast cancer pattern using artificial neural networks and multivariate adaptive regression splines. Expert Systems with Applications, 2004. 27(1): p. 133-142.
[44] Han, J. and M. Kamber, Data Mining : Concepts and Techniques. 2006, San Francisco: Morgan Kaufmann Publisher.
[45] Lynne, M., Talent Management Value Imperatives : Strategies for Execution. 2005, The Conference Board.
[46] Cubbingham, I., Talent Management : Making it real. Development and Learning in Organizations, 2007. 21(2): p. 4-6.
[47] CHINA UPDATE (2007) HR News for Your Organization : The Tower Perrin Asia Talent Management Study.
[48] Chen, S.H. and H.T. Lee, Performance evaluation model for project managers using managerial practices. International Journal of Project Management, 2007. 25: p. 543-551.