An Optimized Grey Cluster Model for Evaluating Quality of Labor Force

In this study, the concept of relative membership degree is proposed to investigate grey optimal cluster evaluation. Besides, the correlation coefficient between sample observation value and standard eigenvalue is used to reflect the similarity degree of evaluation objective and each cluster centre. Based on those mentioned above and concept of relative membership degree, an optimized grey cluster evaluation model is established. Meanwhile, Lagrange function is constructed to obtain the relative membership degree. According to the size of relative membership degree, evaluation objectives are classified. Thereby, the fuzzy membership degree information in classification is effectively integrated into grey cluster evaluation. Finally, the effectiveness and practicability of this model is verified through the labor force quality evaluation of three provinces in East China.