Comparing data mining approach and regression method in determining factors affecting the selection of human resources

Mohammad Vahid SEBT, Hormoz YOUSEFI
1.950 358

Abstract


Abstract. Appropriate selection of human resources especially human-centered and service organizations has a direct impact on the delivery of products and services of that organization. So far, a variety of methods have been provided by management experts to select people in organizations. One of these methods is using historical data to apply in future selections. Data mining as an effective knowledge has been considered less in this field and generally, it has been limited to simple statistical methods in this regard. In this regard and in this paper, the decision tree technique algorithms as one of the most effective techniques of data mining have been used in addition to use recruitment test database of a commercial bank to investigate the factors that affect the performance and promotion of human resources and compare their results with statistical methods. One of the obtained results is removing the performance assessment variable as the target variable in the data mining method which is due to the lack of precision in the process of evaluating the studied institution's performance assessment. This result is obtained when there is a relationship between performance assessment and at least two variables, according to the regression method. Also, in this research it was indicated that 5 variables of "test total score", "interview score", "educational level", "professional experience", and "province of service site" are effective on improving the volunteers among 26 studied variables. These results have led to the knowledge which has been explained by the experts and converted in the form of recommendations for banks.


Keywords


Selection of human resources, data mining, knowledge discovery, decision tree, regression

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