Abstract: Human capital is of a high concern for companies’ management where their most interest is in hiring the highly qualified personnel which are expected to perform highly as well. Recently, there has been a growing interest in the data mining area, where the objective is the discovery of knowledge that is correct and of high benefit for users. In this paper, data mining techniques were utilized to build a classification model to predict the performance of employees. To build the classification model the CRISP-DM data mining methodology was adopted. Decision tree was the main data mining tool used to build the classification model, where several classification rules were generated. To validate the generated model, several experiments were conducted using real data collected from several companies. The model is intended to be used for predicting new applicants’ performance.

Qasem A Al-Radaideh and Eman Al Nagi, “Using Data Mining Techniques to Build a Classification Model for Predicting Employees Performance” International Journal of Advanced Computer Science and Applications(IJACSA), 3(2), 2012. http://dx.doi.org/10.14569/IJACSA.2012.030225

@article{Al-Radaideh2012,
title = {Using Data Mining Techniques to Build a Classification Model for Predicting Employees Performance},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2012.030225},
url = {http://dx.doi.org/10.14569/IJACSA.2012.030225},
year = {2012},
publisher = {The Science and Information Organization},
volume = {3},
number = {2},
author = {Qasem A Al-Radaideh and Eman Al Nagi}
}