Model Design for Personnel Selection with Data Mining Approach (Case Study: A Commerce Bank of Iran)

The success or failure of an organization has a direct relationship with how its human resources are employed and retained. It is the case that organizations keep large amounts of information and data on entrance evaluations and processes. This information, however, is often left unutilized. Data mining is considered a solution for analyzing these data. This paper is investigating educated and objective methods of data analysis. It follows statistical rules, data mining techniques, and the relationship between entrance evaluation scores and personal and professional variables. These factors are studied in order to determine the assignment and rank of potential employees. The database and personnel information of the a Commerce Bank of Iran (in years of 2005 and 2006) is studied and analyzed as a case study in order to identify the labor factors which are considered effective in job performance. The data mining technique that is used in this project serves as the decision-tree. Rules Derivation has been accomplished by the QUEST, CHAID, C5.0 and CART algorithms. The objective and the appropriate algorithms are determined based on seemingly “irrelevant” components, which the Commerce Bank Human Resources management experts described. Results indicated not taking into account the “performance assessment” variable as the objective. Also this project has identified the following from 26 variables have been investigated, five variables as the effective factors in employee promotion: examination score, interview score, degree, years of experience, and job location. The paper's results led in knowledge that can be practical.