DocumentCode :
2831777
Title :
Potential Data Mining Classification Techniques for Academic Talent Forecasting
Author :
Jantan, Hamidah ; Hamdan, Abdul Razak ; Othman, Zulaiha Ali
Author_Institution :
Fac. of Comput. & Math. Sci., Univ. Teknol. MARA (UiTM) Terengganu, Dungun, Malaysia
fYear :
2009
fDate :
Nov. 30 2009-Dec. 2 2009
Firstpage :
1173
Lastpage :
1178
Abstract :
Classification and prediction are among the major techniques in data mining and widely used in various fields. In this article we present a study on how some talent management problems can be solved using classification and prediction techniques in data mining. By using this approach, the talent performance can be predicted by using past experience knowledge discovered from the existing database. In the experimental phase, we have used selected classification and prediction techniques to propose the appropriate techniques from our training dataset. An example is used to demonstrate the feasibility of the suggested classification techniques using academician performance data. Thus, by using the experiments results, we suggest the potential classification techniques for academic talent forecasting.
Keywords :
data mining; academic talent forecasting; knowledge discovery; potential data mining classification techniques; prediction techniques; training dataset; Application software; Data mining; Databases; Electronic mail; Employee rights; Human resource management; Information science; Intelligent systems; Statistical analysis; Technology forecasting; Academic Talent and Forecasting; Classification Techniques; Data Mining;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Systems Design and Applications, 2009. ISDA '09. Ninth International Conference on
Conference_Location :
Pisa
Print_ISBN :
978-1-4244-4735-0
Electronic_ISBN :
978-0-7695-3872-3
Type :
conf
DOI :
10.1109/ISDA.2009.64
Filename :
5364167
Link To Document :
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