• 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