| Abstract:
The success or failure of any organization is directly linked to the quality of its human resources selection (recruitment, measurement, and selection). By reviewing the data of a knowledge job, this paper aims to help improve the selection process of that job. Consequently, the selection of appropriate employees’ rate will increase, and the rate of human resource turnover will decrease. The approach of this paper is Applied Research and the strategy is Case Study. This paper combines two computational techniques (DEA and CART). Data Envelopment Analysis (DEA) is a non-parametric technique that determines the efficiency of individuals, but it does not provide information on the details of factors affecting performance (especially non-numerical factors). In the present study, this deficiency has been resolved using the Classification and Regression Tree (CART) (as a data mining technique). The result of this study has provided a framework for combining DEA and CART in order to discover rules on the recruitment of knowledge workers in a specific job (a knowledge job) and in a specific organization (HFJ Institute). The results indicate that ‘ work experience’ , ‘ average score in the last degree’ and ‘ age’ are related to the employee performance, and therefore it is necessary to be considered in the process of future recruitment of that job.
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