TY - JOUR ID - 10857 TI - A data mining approach to employee turnover prediction (case study: Arak automotive parts manufacturing) JO - Journal of Industrial and Systems Engineering JA - JISE LA - en SN - 1735-8272 AU - Esmaieeli Sikaroudi, Amir Mohammad AU - Ghousi, Rouzbeh AU - Sikaroudi, Ali AD - Iran University of Science and Technology AD - Industrial & Manufacturing Engineering Department Florida State University,USA Y1 - 2015 PY - 2015 VL - 8 IS - 4 SP - 106 EP - 121 KW - Employees’ turnover KW - Data mining KW - Human Resource Management KW - recruitment decision support system DO - N2 - Training and adaption of employees are time and money consuming. Employees’ turnover can be predicted by their organizational and personal historical data in order to reduce probable loss of organizations. Prediction methods are highly related to human resource management to obtain patterns by historical data. This article implements knowledge discovery steps on real data of a manufacturing plant. We consider many characteristics of employees such as age, technical skills and work experience. Different data mining methods are compared based on their accuracy, calculation time and user friendliness. Furthermore the importance of data features is measured by Pearson Chi-Square test. In order to reach the desired user friendliness, a graphical user interface is designed specifically for the case study to handle knowledge discovery life cycle. UR - https://www.jise.ir/article_10857.html L1 - https://www.jise.ir/article_10857_380ab2c2c84e1525e1f53647b46d6879.pdf ER -