The search for employees aligned to the companies needs and with a high performance profile has led them to adopt several approaches for recruiting, selecting and evaluating in order to identify the candidates that best fit to the requirements of the position. There are many different approaches used but the companies, however, the decision about what is the most effective in terms of personnel performance management is a hard task. In this paper, a study based in data mining techniques is presented to help managers in this process. The common practice, used by the companies, is classify the candidates according the personal characteristics that are necessary to achieve a good performance using normally internal/personal characteristics more than external/environmental influences as determinant for the behaviors. The existing tools can be categorized according to their objectives, ranging from the selection process to the measurement of the performance achieved by the employees. Usually, these tools are not interconnected and the information generated by them does not allow a consistent analysis of the different stages of the employee and his professional evolution. It was mapped the different standards of leadership profile and performance results of the employees that work a specific company. Also, a classifier based in the Combinatorial Neural Model was built to find the relations between the personal characteristics and performance results.
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