Classification and prediction of academic talent using data mining techniques

Authors:
Hamidah Jantan;Abdul Razak Hamdan;Zulaiha Ali Othman
Affiliations:
Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA Terengganu, Terengganu, Malaysia;Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Selangor, Malaysia;Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Selangor, Malaysia
Venue:
KES'10 Proceedings of the 14th international conference on Knowledge-based and intelligent information and engineering systems: Part I
Year:
2010

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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.