Investigating data mining algorithms in predicting the Job position of personnel and proposing appropriate algorithm

Selection of decent and capable individuals for key positions and attention to the principle of merit selection in the distribution of power and division of duties and responsibilities among the elites of society, in addition to contributing to the growth, progress and stability of the country, is a code of the success and survival of the rulers. Choosing the decent individuals in the administrative system is a form of management in which personnel are employed for their individual competence and merits. The purpose of this study is to investigate the job position of employed personnel of the Islamic revolutionary guard corps (IRGC) based on individual characteristics as well as the use of data mining to predict the job position of new employees. For predicting the job position of each employee, after preprocessing steps, personnel's characteristics are supervised based on five machine learning algorithms and after analyzing them, the accuracy of each one of these algorithms is compared to be used in a recommender system. The decision tree algorithm is introduced with 97/90% classification accuracy as the best data mining algorithm in a recommender system for predicting the appropriate post of each employee, and also, on that basis, the Naive Bayes algorithm is the most inappropriate algorithm for this purpose. The value of the present study is that based on personal information, characteristics and competencies, determines the job position of current or new personnel and in this approach, relation is not preferred over specialty, education or other important individual characteristics. Also, utilizing algorithms that are mentioned in this article leads to optimal organization of personnel in a way that each person would be organized in the most appropriate Job position with the least error.