Discovering Interesting Association Rules from Student Admission Dataset

  • Zailani Abdullah
  • Tutut Herawan
  • Mustafa Mat Deris
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 285)

Abstract

Finding the interesting rules from data repository is quite challenging weather for public or private sectors practitioners. Therefore, the purpose of this study is to apply an enhanced association rules mining method, so called SLP-Growth (Significant Least Pattern Growth) proposed by [11,36] to mining the interesting association rules based on the student admission dataset. The dataset contains the records of preferred programs being selected by post-matriculation or post-STPM students of Malaysia via Electronic Management of Admission System (e-MAS) for the year 2008/2009. The results of this study will provide useful information for educators and higher university authority personnel in the university to understand the programs’ patterns being selected by them.

Keywords

Association rule mining Significant least patterns Students 

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Notes

Acknowledgments

The authors would like to thanks Universiti Malaysia Terengganu for supporting this work. The work of Tutut Herawan is supported by Excellent Research Grant Scheme no vote O7/UTY-R/SK/0/X/2013 from Universitas Teknologi Yogyakarta, Indonesia.

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Copyright information

© Springer Science+Business Media Singapore 2014

Authors and Affiliations

  • Zailani Abdullah
    • 1
  • Tutut Herawan
    • 2
    • 3
  • Mustafa Mat Deris
    • 4
  1. 1.Department of Computer ScienceUniversiti Malaysia TerengganuKuala TerengganuMalaysia
  2. 2.Faculty of Computer Science and Information TechnologyUniversiti MalayaKuala LumpurMalaysia
  3. 3.Universitas Teknologi YogyakartaYogyakartaIndonesia
  4. 4.Faculty of Science Computer and Information TechnologyUniversiti Tun Hussein Onn MalaysiaBatu PahatMalaysia

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