Application of Statistical K-Means Algorithm for University Academic Evaluation
With the globalization of higher education, academic evaluation is increasingly valued by the scientific and educational circles. Although the number of published papers of academic evaluation methods is increasing, previous research mainly focused on the method of assigning different weights for va...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-07-01
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| Series: | Entropy |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1099-4300/24/7/1004 |
| _version_ | 1797406526060625920 |
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| author | Daohua Yu Xin Zhou Yu Pan Zhendong Niu Huafei Sun |
| author_facet | Daohua Yu Xin Zhou Yu Pan Zhendong Niu Huafei Sun |
| author_sort | Daohua Yu |
| collection | DOAJ |
| description | With the globalization of higher education, academic evaluation is increasingly valued by the scientific and educational circles. Although the number of published papers of academic evaluation methods is increasing, previous research mainly focused on the method of assigning different weights for various indicators, which can be subjective and limited. This paper investigates the evaluation of academic performance by using the statistical K-means (SKM) algorithm to produce clusters. The core idea is mapping the evaluation data from Euclidean space to Riemannian space in which the geometric structure can be used to obtain accurate clustering results. The method can adapt to different indicators and make full use of big data. By using the K-means algorithm based on statistical manifolds, the academic evaluation results of universities can be obtained. Furthermore, through simulation experiments on the top 20 universities of China with the traditional K-means, GMM and SKM algorithms, respectively, we analyze the advantages and disadvantages of different methods. We also test the three algorithms on a UCI ML dataset. The simulation results show the advantages of the SKM algorithm. |
| first_indexed | 2024-03-09T03:26:45Z |
| format | Article |
| id | doaj.art-c740e60aeb694a68b1e05e7228b51633 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-09T03:26:45Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-c740e60aeb694a68b1e05e7228b516332023-12-03T15:01:23ZengMDPI AGEntropy1099-43002022-07-01247100410.3390/e24071004Application of Statistical K-Means Algorithm for University Academic EvaluationDaohua Yu0Xin Zhou1Yu Pan2Zhendong Niu3Huafei Sun4School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, ChinaSchool of Mathematics and Statistics, Beijing Institute of Technology, Beijing 100081, ChinaSchool of Mathematics and Statistics, Beijing Institute of Technology, Beijing 100081, ChinaSchool of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, ChinaSchool of Mathematics and Statistics, Beijing Institute of Technology, Beijing 100081, ChinaWith the globalization of higher education, academic evaluation is increasingly valued by the scientific and educational circles. Although the number of published papers of academic evaluation methods is increasing, previous research mainly focused on the method of assigning different weights for various indicators, which can be subjective and limited. This paper investigates the evaluation of academic performance by using the statistical K-means (SKM) algorithm to produce clusters. The core idea is mapping the evaluation data from Euclidean space to Riemannian space in which the geometric structure can be used to obtain accurate clustering results. The method can adapt to different indicators and make full use of big data. By using the K-means algorithm based on statistical manifolds, the academic evaluation results of universities can be obtained. Furthermore, through simulation experiments on the top 20 universities of China with the traditional K-means, GMM and SKM algorithms, respectively, we analyze the advantages and disadvantages of different methods. We also test the three algorithms on a UCI ML dataset. The simulation results show the advantages of the SKM algorithm.https://www.mdpi.com/1099-4300/24/7/1004statistical K-meansacademic evaluationstatistical manifoldclustering |
| spellingShingle | Daohua Yu Xin Zhou Yu Pan Zhendong Niu Huafei Sun Application of Statistical K-Means Algorithm for University Academic Evaluation Entropy statistical K-means academic evaluation statistical manifold clustering |
| title | Application of Statistical K-Means Algorithm for University Academic Evaluation |
| title_full | Application of Statistical K-Means Algorithm for University Academic Evaluation |
| title_fullStr | Application of Statistical K-Means Algorithm for University Academic Evaluation |
| title_full_unstemmed | Application of Statistical K-Means Algorithm for University Academic Evaluation |
| title_short | Application of Statistical K-Means Algorithm for University Academic Evaluation |
| title_sort | application of statistical k means algorithm for university academic evaluation |
| topic | statistical K-means academic evaluation statistical manifold clustering |
| url | https://www.mdpi.com/1099-4300/24/7/1004 |
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