The Application of Data Mining Techniques in Employee Performance Assessment

Abstract

Employee performance assessment is a powerful standard for measuring talent, and many companies pay more attention to the assessment of employee performance. Currently, there are many kinds of methods for employee performance evaluation. This leads to deficiencies in the data accuracy and data mining of current performance research. Therefore, to enhance the deep-level mining of performance data, the advantages of using methods are emphasized. This research uses data mining technology to measure employee performance and builds an improved ID3 decision tree algorithm model based on data mining technology, which can measure deeper employee performance. The experimental results show that the algorithm model is able to measure employee performance well, the accuracy of the decision tree algorithm is 93.2%, and the accuracy of the improved algorithm is 95.3%, so the improved algorithm is 39 ms shorter than the traditional algorithm in building the decision tree, and the algorithm accuracy is 2.1% higher. This shows that the improved decision tree algorithm of data mining technology can improve the precision and accuracy of employee performance evaluation.