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Human Talent Forecasting using Data Mining Classification Techniques

Human Talent Forecasting using Data Mining Classification Techniques

Hamidah Razak Jantan, Abdul Ali Hamdan, Zulaiha Othman
Copyright: © 2010 |Volume: 1 |Issue: 4 |Pages: 13
ISSN: 1947-9301|EISSN: 1947-931X|EISBN13: 9781613502198|DOI: 10.4018/jtd.2010100103
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MLA

Jantan, Hamidah Razak, et al. "Human Talent Forecasting using Data Mining Classification Techniques." IJTD vol.1, no.4 2010: pp.29-41. http://doi.org/10.4018/jtd.2010100103

APA

Jantan, H. R., Hamdan, A. A., & Othman, Z. (2010). Human Talent Forecasting using Data Mining Classification Techniques. International Journal of Technology Diffusion (IJTD), 1(4), 29-41. http://doi.org/10.4018/jtd.2010100103

Chicago

Jantan, Hamidah Razak, Abdul Ali Hamdan, and Zulaiha Othman. "Human Talent Forecasting using Data Mining Classification Techniques," International Journal of Technology Diffusion (IJTD) 1, no.4: 29-41. http://doi.org/10.4018/jtd.2010100103

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Abstract

Talent management is a very crucial task and demands close attention from human resource (HR) professionals. Recently, among the challenges for HR professionals is how to manage organization’s talents, particularly to ensure the right job for the right person at the right time. Some employee’s talent patterns can be identified through existing knowledge in HR databases, which data mining can be applied to handle this issue. The hidden and useful knowledge that exists in databases can be discovered through classification task and has been widely used in many fields. However, this approach has not successfully attracted people in HR especially in talent management. In this regard, the authors attempt to present an overview of talent management problems that can be solved by using this approach. This paper uses that approach for one of the talent management tasks, i.e., predicting potential talent using previous existing knowledge. Future employee’s performances can be predicted based on past experience knowledge discovered from existing databases by using classification techniques. Finally, this study proposes a framework for talent forecasting using the potential Data Mining classification techniques.

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