Evaluating employee performance with an improved clustering algorithm
This paper introduced performance appraisal management, analyzed its principles, proposes seven appraisal indicators, put forward an improved K-means algorithm for the classification of appraisal results based on the K-means clustering algorithm and density parameters, and analyzed the performance of the method. The results of the UCI data set showed that the accuracy rate of the improved K-means algorithm was significantly higher than that of the traditional K-means algorithm, and its highest accuracy rate was 91.27%. The classification of the appraisal results of 45 employees in Company A showed that the classification results obtained using the improved K-means algorithm were more in line with the actual situation and more reasonable and relevant than the traditional scoring method. The appraisal results of 1064 technical employees showed that most of the employees had a good performance. The results of the experiment verify the effectiveness of the improved K-means algorithm in performance appraisal management. The algorithm can be further promoted and applied in practice.
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