Classification and prediction of academic talent using data mining techniques

• Published in: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
• Main Author: Jantan H.; Hamdan A.R.; Othman Z.A.
• Format: Conference paper
• Language: English
• Published: 2010
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-78449249359&doi=10.1007%2f978-3-642-15387-7_53&partnerID=40&md5=1fa97e03b259282c8cca487efaf71a58

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. © 2010 Springer-Verlag.