Personality Typology of Violent NAJA Employees and Provide Clustering Pattern and Predicting Their Violations by Using Data Mining

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SHARIFI MORTEZA

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Journal: JOURNAL OF NAJA MANPOWER Year:2020 | Volume:13 | Issue:58 Page(s): 27-46

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Title

Personality Typology of Violent NAJA Employees and Provide Clustering Pattern and Predicting Their Violations by Using Data Mining

Author(s)

SHARIFI MORTEZA | Issue Writer Certificate

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Abstract

The typology and personality of employees in different organizations is a new, important and necessary issue. The application of this issue in human resource management causes an increasing tendency to recognize the personality traits of employees and has led to the development of various personality typology tests. Therefore, the study of the relationship between different personality types with topics such as productivity, motivation and employee abuse rate has been considered by researchers in the field of management. The present study aims to identify and categorize the offender’ s staff and provide a scientific model for predicting offenses using data mining techniques. This research is applied and is a descriptive survey that has been implemented in two phases related to each other. The first phase of the personality typology of the offender and the second phase of the model and the clustering of prediction of employee abuse on the basis of data mining has been presented. The results of the first phase of the research indicate that the highest frequency of personality types among the offender staff belongs to the four types of personality “ ISTJ” , “ ESTJ” , “ ISTP” and “ INTJ” respectively. These four types constitute 78. 41% of the total number of interviewees. The “ financial crimes” have been the most frequent crime committed by the group. The following titles belong to “ moral crimes” , “ drug crimes” , and “ special military offenses” . Also, the results of the second phase show that clandestine employees are clustered in three main clusters, and the categorization patternand employee offense prediction were calculated in a 10-dimensional model.

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