Journal of Jilin University(Engineering and Technology Edition) ›› 2021, Vol. 51 ›› Issue (5): 1851-1856.doi: 10.13229/j.cnki.jdxbgxb20201004

Previous Articles    

Design of fuzzy comprehensive evaluation system for performance appraisal based on K⁃means clustering algorithm

Meng-su ZHANG1(),Chun-tian LIU1,Xi-jin LI1,Yong-ping HUANG2()   

  1. 1.Human Resources Division,Jilin University,Changchun 130012,China
    2.College of Computer Science and Technology,Jilin University,Changchun 130012,China
  • Received:2020-12-30 Online:2021-09-01 Published:2021-09-16
  • Contact: Yong-ping HUANG E-mail:zms@jlu.edu.cn;hyp@jlu.edu.cn

Abstract:

Traditional teaching quality evaluation methods require a large amount of human and material resources to be uniformly allocated and the input data are not stored in a uniform way. Such methods suffer from low evaluation efficiency and low evaluation accuracy. Therefore, this paper proposes a design method of teaching quality fuzzy comprehensive evaluation system based on K-means clustering algorithm, which constitutes the overall structure of teaching quality fuzzy comprehensive evaluation system through data preparation module, teaching quality fuzzy evaluation module, report processing module and system maintenance module. The hierarchical analysis method is used to calculate the weights corresponding to the talent quality evaluation indexes, construct the teaching quality fuzzy evaluation model, solve the teaching quality fuzzy comprehensive evaluation model by K-means clustering algorithm, realize the evaluation of teaching quality, and complete the design of the teaching quality fuzzy comprehensive evaluation system. The experimental results show that the proposed method has high evaluation efficiency and high evaluation accuracy.

Key words: computer application, K-means clustering algorithm, teaching quality, fuzzy evaluation, system design

CLC Number: 

  • TP18

Fig.1

Structure of fuzzy evaluation system for performance appraisal"

Fig.2

Block diagram of evaluation procedure"

Fig.3

Comprehensive evaluation for performance appraisal"

Table 1

Evaluation accuracy of different methods"

迭代次数本文算法模糊综合评判系统设计方法
10.650.61
20.720.69
30.780.72
40.830.76
50.920.84
60.850.79

Table 2

Evaluation time of different methods"

迭代次数本文算法证据推理系统设计方法
10.621.32
20.651.02
30.741.62
40.671.36
50.621.26
60.581.06
1 宋晨霞, 张海光, 胡庆夕. 高校示范中心全方位绩效考核的探索与实践[J]. 实验技术与管理, 2019, 36(7): 197-200.
Song Chen-xia, Zhang Hai-guang, Hu Qing-xi. Exploration and practice of all-sided performance evaluation on university demonstration centers[J]. Experimental Technology and Management, 2019, 36(7):197-200.
2 陈丽娟, 陈滨, 刘海霞. 厦门大学图书馆的绩效考核评价体系[J]. 图书馆论坛, 2020, 40(1): 146-152.
Chen Li-juan, Chen Bin, Liu Hai-xia. The performance appraisal system of Xiamen University Library[J]. Library Tribune, 2020, 40(1): 146-152.
3 Deng X, Gu Y, Li F, et al. Evaluation of teaching quality of computing method course based on improved BP neural network[J]. Journal of Physics Conference Series, 2021, 1774: 012026.
4 李锋, 林华. 基于功效系数法与模糊综合评价法的企业营销绩效考核研究[J]. 学术论坛, 2010, 33(2): 113-116.
Li Feng, Lin Hua. Research on enterprise marketing performance evaluation based on efficiency coefficient method and fuzzy comprehensive evaluation method [J]. Academic Forum, 2010, 33(2): 113-116.
5 李桂英. 基于模糊综合评判授课质量评价系统的设计与实现[J]. 华南师范大学学报: 自然科学版, 2003(4): 48-53.
Li Gui-ying. Research and design of teaching quality evaluation system based on fuzzy comprehensive evaluation[J]. Joutnal of South China Normal University (Natural Science Edition), 2003(4): 48-53.
6 Zhao Y. Research on the application of university teaching management evaluation system based on Apriori algorithm[J]. Journal of Physics, 2021, 1883: 012033.
7 李晓霞. 基于模糊综合评价模型的教学评价系统的设计与实现[D]. 成都: 电子科技大学计算机科学与工程学院, 2015.
Li Xiao-xia. Design and implementation of teaching evaluation system on fuzzy evaluation[D]. Chengdu: School of Computer Science and Engineering, University of Electronic Science and Technology of China, 2015.
8 杨洁, 黄梦亿, 陈佳. 多粒度邻域粗糙模糊集及其不确定性度量[J]. 重庆邮电大学学报:自然科学版, 2020, 32(5): 898-908.
Yang Jie, Huang Meng-yi, Chen Jia. Neighborhood-based multi-granulation rough fuzzy sets and their uncertainty measure[J]. Journal of Chongqing University of Posts and Telecommunications (Natural Science Edition), 2020, 32(5): 898-908.
9 张波, 周从华, 张付全, 等. 一种面向SNP选择的模糊聚类算法[J]. 计算机工程, 2019, 45(8): 66-74.
Zhang Bo, Zhou Cong-hua, Zhang Fu-quan,et al. A Fuzzy Clustering Algorithm for SNP Selection[J]. Computer Engineering, 2019, 45(8): 66-74.
10 祖志文, 李秦. 基于粒子群优化的马氏距离模糊聚类算法[J]. 重庆邮电大学学报:自然科学版,2019, 31(2): 279-384.
Zu Zhi-wen and Li Qin. Mahalanobis distance fuzzy clustering algorithm based on particle-swarmoptimization[J]. Journal of Chongqing University of Posts and Telecommunications(Natural Science Edition),2019, 31(2): 279-384.
11 钱雪忠,姚琳燕. 面向稀疏高维大数据的扩展增量模糊聚类算法[J]. 计算机工程, 2019, 45(6): 75-81.
Qian Xue-zhong,Yao Lin-ya. Extended incremental fuzzy clustering algorithm for sparse high-dimensional big data[J]. Computer Engineering, 2019, 45(6): 75-81.
12 丁志成, 葛洪伟, 周竞. 基于KL散度的密度峰值聚类算法[J]. 重庆邮电大学学报:自然科学版,2019, 31(3): 367-374.
Ding Zhi-cheng, Ge Hong-wei,Zhou Jing. Density peaks clustering based on Kullback Leibler divergence[J]. Journal of Chongqing University of Posts and Telecommunications(Natural Science Edition), 2019, 31(3): 367-374.
13 徐勇, 张慧, 陈亮. 一种基于情感分析的UGC模糊综合评价方法——以淘宝商品文本评论UGC为例[J]. 情报理论与实践, 2016, 39(6): 64-69.
Xu Yong, Zhang Hui, Chen Liang. A UGC fuzzy comprehensive evaluation method based on sentiment analysis—taking UGC of Taobao Product text reviews[J]. Information Studies: Theory & Application, 2016, 39(6): 64-69.
14 王茜竹, 徐瑞, 江德潮, 雒江涛. 基于多源数据的出行安全时空评价模型研究[J]. 重庆邮电大学学报:自然科学版, 2019, 31(5): 618-627.
Jiang De-chao, Luo Jiang-tao. Research on the spatial-temporal evaluation model of travel safety based on multi-source data[J]. Journal of Chongqing University of Posts and Telecommunications (Natural Science Edition), 2019, 31(5): 618-627.
15 MacQueen J. Some methods for classification and analysis of multivariate observations[J]. Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, 1967, 14(1): 281-297.
[1] Jie CAO,Xue QU,Xiao-xu LI. Few⁃shot image classification method based on sliding feature vectors [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(5): 1785-1791.
[2] Yin-di YAO,Jun-jin HE,Yang-li LI,Dang-yuan XIE,Ying LI. ET0 simulation of self⁃constructed improved whale optimized BP neural network [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(5): 1798-1807.
[3] Hong-wei ZHAO,Zi-jian ZHANG,Jiao LI,Yuan ZHANG,Huang-shui HU,Xue-bai ZANG. Bi⁃direction segmented anti⁃collision algorithm based on query tree [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(5): 1830-1837.
[4] Chun-bo WANG,Xiao-qiang DI. Cloud storage integrity verification audit scheme based on label classification [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(4): 1364-1369.
[5] Dan-tong OUYANG,Yang LIU,Jie LIU. Fault diagnosis method based on test set under fault response guidance [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(3): 1017-1025.
[6] Rong QIAN,Ru ZHANG,Ke-jun ZHANG,Xin JIN,Shi-liang GE,Sheng JIANG. Capsule graph neural network based on global and local features fusion [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(3): 1048-1054.
[7] Xiao-long ZHU,Zhong XIE. Geospatial data extraction algorithm based on machine learning [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(3): 1011-1016.
[8] Bao-feng SUN,Xin-xin REN,Zai-si ZHENG,Guo-yi Li. Multi⁃objective flow shop optimal scheduling considering worker's load [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(3): 900-909.
[9] Shu-min WANG,Wei CHEN. Algorithm for identifying abnormal behavior in underground mines based on continuous density hidden Markov model [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(3): 1067-1072.
[10] Yuan-ning LIU,Di WU,Xiao-dong ZHU,Qi-xian ZHANG,Shuang-shuang LI,Shu-jun GUO,Chao WANG. User interface components detection algorithm based on improved YOLOv3 [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(3): 1026-1033.
[11] Guang-qiu CHEN,Yu-cun CHEN,Jia-yue LI,Guang-wen LIU. Infrared and visible image fusion based on discrete nonseparable shearlet transform and convolutional sparse representation [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(3): 996-1010.
[12] Shu-tao SHEN,Zha-xi NIMA. Double chaos identifiable tampering image encryption method based on blockchain technology [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(3): 1055-1059.
[13] Bing-hai ZHOU,Zhao-xu HE. Static semi⁃kitting strategy⁃based multi⁃objective just⁃in⁃time material distribution scheduling [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(3): 910-916.
[14] Tian-qi GU,Chen-jie HU,Yi TU,Shu-wen LIN. Robust reconstruction method based on moving least squares algorithm [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(2): 685-691.
[15] Qian-yi XU,Gui-he QIN,Ming-hui SUN,Cheng-xun MENG. Classification of drivers' head status based on improved ResNeSt [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(2): 704-711.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!