Algorithms for effective team building

Abstract

Effective team building is an important issue of human resource management (HRM). In order to keep up with technological improvements and changes, selecting the right person for the right job position is very important. This paper describes a research and development methodology for establishing a more sophisticated approach for composing effective teams. Data mining (DM) techniques and algorithms, like decision trees, Bayesian networks and fuzzy logic, were utilized to build a model to predict the best possible person for a specific job. We have applied K-means and fuzzy C-means clustering and decision tree classification algorithms. Pruned and unpruned trees were contributed using ID3, C4.5 and CART algorithms. By using these techniques, the patterns of employee performance were generated. To validate the generated model, several experiments were conducted using data collected from IT companies. After evaluation, the most appropriate algorithms are recommended to be used in the process of effective team building.