Abstract
In talent management, process to identify a potential talent is among the crucial tasks and need highly attentions from human resource professionals. Nowadays, data mining (DM) classification and prediction techniques are widely used in various fields. However, this approach has not attracted much interest from people in human resource. In this article, we attempt to determine the potential classification techniques for academic talent forecasting in higher education institutions. Academic talents are considered as valuable human capital which is the required talents can be classified by using past experience knowledge discovered from related databases. As a result, the classification model will be used for academic talent forecasting. In the experimental phase, we have used selected DM classification techniques. The potential technique is suggested based on the accuracy of classification model generated by that technique. Finally, the results illustrate there are some issues and challenges rise in this study, especially to acquire a good classification model.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufmann Publisher, San Francisco (2006)
Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann Publisher, San Francisco (2005)
Chien, C.F., Chen, L.F.: Data mining to improve personnel selection and enhance human capital: A case study in high-technology industry. Expert Systems and Applications 34, 380–390 (2008)
Wang, H., Wang, S.: A knowledge management approach to data mining process for business intelligence. Industrial Management & Data Systems 108, 622–634 (2008)
Ranjan, J.: Data Mining Techniques for better decisions in Human Resource Management Systems. International Journal of Business Information Systems 3, 464–481 (2008)
Cubbingham, I.: Talent Management: Making it real. Development and Learning in Organizations 21, 4–6 (2007)
Cappelli, P.: Talent Management for the Twenty-First Century, http://www.hbr.org
A TP Track Research Report Talent Management: A State of the Art. Tower Perrin HR Services (2005)
Jantan, H., Hamdan, A.R., Othman, Z.A.: Knowledge Discovery Techniques for Talent Forecasting in Human Resource Application. In: World Academy of Science, Engineering and Technology, Penang, Malaysia, pp. 803–811 (2009)
Huang, M.J., Tsou, Y.L., Lee, S.C.: Integrating fuzzy data mining and fuzzy artificial neural networks for discovering implicit knowledge. Knowledge-Based Systems 19, 396–403 (2006)
Chien, C.F., Chen, L.F.: Using Rough Set Theory to Recruit and Retain High-Potential Talents for Semiconductor Manufacturing. IEEE Transactions on Semiconductor Manufacturing 20, 528–541 (2007)
Tung, K.Y., Huang, I.C., Chen, S.L., Shih, C.T.: Mining the Generation Xer’s job attitudes by artificial neural network and decision tree-empirical evidence in Taiwan. Expert Systems and Applications 29, 783–794 (2005)
Chen, K.K., Chen, M.Y., Wu, H.J., Lee, Y.L.: Constructing a Web-based Employee Training Expert System with Data Mining Approach. In: The 9th IEEE International Conference on E-Commerce Technology and The 4th IEEE International Conference on Enterprise Computing, E-Commerce and E-Services, CEC-EEE 2007 (2007)
Lynne, M.: Talent Management Value Imperatives: Strategies for Execution. In: The Conference Board (2005)
CHINA UPDATE. HR News for Your Organization: The Tower Perrin Asia Talent Management Study, http://www.towersperrin.com
Jantan, H., Hamdan, A.R., Othman, Z.A.: Data Mining Techniques for Performance Prediction in Human Resource Application. In: 1st Seminar on Data Mining and Optimization, pp. 41–49. CAIT UKM, Selangor (2008)
Tso, G.K.F., Yau, K.K.W.: Predicting electricity energy consumption: A comparison of regression analysis, decision tree and neural networks. Energy 32, 1761–1768 (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Jantan, H., Hamdan, A.R., Othman, Z.A. (2010). Classification and Prediction of Academic Talent Using Data Mining Techniques. In: Setchi, R., Jordanov, I., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based and Intelligent Information and Engineering Systems. KES 2010. Lecture Notes in Computer Science(), vol 6276. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15387-7_53
Download citation
DOI: https://doi.org/10.1007/978-3-642-15387-7_53
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15386-0
Online ISBN: 978-3-642-15387-7
eBook Packages: Computer ScienceComputer Science (R0)