Evaluating employee performance with an improved clustering algorithm

Ci Fan


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.

Full Text:



Moradi T, Mehraban M A, Moeini M (2017). Comparison of the Perceptions of Managers and Nursing Staff Toward Performance Appraisal. Iranian journal of nursing and midwifery research, 22, pp. 128.

Gomez-Mejia LR (2016). Increasing Productivity: Performance Appraisal and Reward Systems. Personnel Review, 2016, 19, pp. 21-26. https://doi.org/10.1108/00483489010138759

Yousif M K, Shaout A (2016). Fuzzy logic computational model for performance evaluation of Sudanese Universities and academic staff. Journal of King Saud University - Computer and Information Sciences, 30, pp. 80-119. https://doi.org/0.1016/j.jksuci.2016.08.002

Ditzian K, Wilder D A, King A, Tanz J (2015). An evaluation of the Performance Diagnostic Checklist-Human Services to assess an employee performance problem in a center-based autism treatment facility. Journal of Applied Behavior Analysis, 48, pp. 199. https://doi.org/10.1002/jaba.171

Bird H (2015). Appraising clever people: lessons from introducing performance reviews for academics in a UK University. Industrial & Commercial Training, 47, pp. 81-85. https://doi.org/10.1108/ICT-10-2014-0069

Karpenko A, Karpenko N, Shudrik O (2020). Development and implementation of a strategic personnel management system according to goals based on key performance indicators. Management and Entrepreneurship Trends of Development, 2, pp. 22-35. https://doi.org/10.26661/2522-1566/2020-2/12-02

Uysal G (2016). Cognitive Placement Theory for Performance Appraisal: Talent Management and Individual Performance. Journal of Modern Accounting and Auditing , 012, pp. 60-63. https://doi.org/10.17265/1548-6583/2016.01.005

Nair M S, Salleh R (2015). Linking Performance Appraisal Justice, Trust, and Employee Engagement: A Conceptual Framework. Procedia - Social and Behavioral Sciences, 211, pp. 1155-1162. https://doi.org/10.1016/j.sbspro.2015.11.154

Andersson G (2016). Data Recording in Performance Management: Trouble With the Logics. American Journal of Evaluation, 38, pp. 190-204. https://doi.org/10.1177/1098214016681510

Lonsdale A (2016). Performance Appraisal, Performance Management and Quality in Higher Education: Contradictions, Issues and Guiding Principles for the Future. Australian Journal of Education, 42, pp. 303-320. https://doi.org/10.1177/000494419804200307

Lv Y, Ma T, Tang M, Cao J, Tian Y, Al-Dhelaan A, Al-Rodhaan M (2016). An efficient and scalable density-based clustering algorithm for datasets with complex structures. Neurocomputing, 171, pp. 9-22. https://doi.org/10.1016/j.neucom.2015.05.109

Guo Y, Sengur A (2015). NCM: Neutrosophic c-means clustering algorithm. Pattern Recognition, 48, pp. 2710-2724. https://doi.org/10.1016/j.patcog.2015.02.018

Dhanachandra N, Manglem K, Chanu Y J (2015). Image Segmentation Using K -means Clustering Algorithm and Subtractive Clustering Algorithm. Procedia Computer Science, 54, pp. 764-771.

Zhang Y, Zhang P (2015). Machine training and parameter settings with social emotional optimization algorithm for support vector machine. Pattern Recognition Letters, 54, pp. 36-42. https://doi.org/10.1016/j.patrec.2014.11.011

DOI: https://doi.org/10.31449/inf.v46i5.4079

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.