Research on Optimization Strategies of Human Resources Management Based on Data Mining

With the rapid development of big data and analysis technology, the application of data mining in the field of human resource management (HRM) has received more and more attention. This study explores how data mining technology can help optimize human resource management strategies, revealing the potential of data analysis in improving recruitment efficiency, employee performance evaluation, and reducing employee turnover. Logistic regression and linear regression models were used to predict and analyze employee satisfaction, performance ratings and turnover probability. The results showed that there was a significant negative correlation between high satisfaction and low turnover rate, while there was a significant negative correlation between long-term work experience and high performance ratings shows a positive correlation. In addition, through the comprehensive application of various data visualization technologies, this article intuitively displays the analysis results and provides data-driven decision support for human resource managers. Although the research has achieved certain results, due to the limitations of the data set and the diversity of model selection, the generalizability and reliability of the research results still need to be further verified. Future work will be dedicated to expanding the data set, exploring more advanced data analysis models, and in-depth research on the specific application effects of data mining technology in human resource management practice.