Explaining and Predicting Graduate Employability Using Data-mining Techniques

(*) Corresponding author

Authors' affiliations

DOI's assignment:
the author of the article can submit here a request for assignment of a DOI number to this resource!
Cost of the service: euros 10,00 (for a DOI)


Data-mining has been applied in various areas because of its ability to rapidly analyze vast amounts of data. For a higher education institution with a huge data of graduate employability, data-mining technique is an appropriate for analyzing this information. This study presents a graduate employability model that uses a classification task to identify the most important causes of graduate employability, and to compare the accuracy between Bayesian methods (Naïve Bayesian, Averaged One-Dependence Estimators, Averaged One-Dependence Estimators with subsumption resolution, Naïve Bayesian Simple, Bayesian networks, and Naïve Bayesian Updateable) and Decision tree methods (J48, J48 Graft, Simple Cart, Random Forest, Logical Analysis of Data tree, Rep tree, Decision Stump, and Random tree). The experiments show that 3 attributes with a direct effect on employability are the work province, occupation type, and times find work. The results can provide valuable insights for domain experts. Moreover, the results of this study show that the J48 Graft algorithm, which is a variant of the Decision tree method, had a higher accuracy than the average of other Bayesian algorithms.
Copyright © 2014 Praise Worthy Prize - All rights reserved.


Bayesian methods, Classification task, Decision tree methods, Data-mining, Graduate employability

Full Text:



  • There are currently no refbacks.

Please send any question about this web site to info@praiseworthyprize.com
Copyright © 2005-2024 Praise Worthy Prize