STEMM Institute Press
Science, Technology, Engineering, Management and Medicine
Research on the Application of Kohonen Neural Networks in Football Clustering Analysis
DOI: https://doi.org/10.62517/jike.202404220
Author(s)
Cenyue Wang, Youn Poong Oh*
Affiliation(s)
Department of Physical Education, Kunsan National University, Jeollabuk-do, Gunsan-si, Republic of Korea, *Corresponding Author.
Abstract
This study applies Kohonen neural networks for clustering analysis in sports. By iteratively optimizing the objective function, it effectively avoids numerous subjective factors, providing a novel and efficient approach for obtaining objective clustering results. The results demonstrate that clustering analysis using Kohonen neural networks offers a clear practical value in evaluating the comprehensive strength of soccer teams. It serves as an effective method for rational, effective, objective, and quantifiable assessment of team tactics and strategies. Furthermore, this method is readily applicable to other comprehensive evaluations in competitive sports.
Keywords
Sports; Clustering Analysis; Kohonen Neural Networks; Soccer
References
[1] Luo Heng. Research on the Construction of a Risk Warning Model for Sports Event Broadcast Rights Operation Based on BP Neural Networks. Sports Science and Technology Literature Bulletin, 2023, 31(05):184-187+221. [2] Chen Deping, Feng Yan. Application of Kohonen Neural Networks in Clustering Analysis of Sports Activities. Sports Science, 2005, 25(3):143-145. [3] Zhao Xiying, Jiang Yu, Liu Peng. Research on a Comprehensive Evaluation Model of Middle School Students' Physical Fitness Based on Clustering Analysis and BP Artificial Neural Networks. Sports Science and Technology, 2022, 43(06):33-36. [4] Huang Yaming, Li Xinwei, Cui Lei, et al. Research on Mutual Citation Relationships of Scientific Journals - A Comparison of the Application of Kohonen Neural Networks and System Clustering Methods. Journal of Medical Informatics, 2007, 28(5):440-444. [5] Luo Zheng, Zhang Xueqian. Detection Model of Malicious Domain Names Based on Evolutionary Thinking Algorithm Optimized S-Kohonen Neural Networks. Information Network Security, 2020, 20(06):82-89. [6] Wen Nan. Bibliometric Report on Research Literature of Ethnic Traditional Sports in Domestic Universities - Visualization Analysis Based on Citespace. Martial Arts Research, 2018, 3(05):116-119+123. [7] Su Yanyan, Qiu Zhiliang, Li Ge, Lu Shenglian, & Chen Ming. A Review of 2D Single-Person Pose Estimation Based on Deep Learning. Computer Engineering and Applications, 2024(07), 1-21. [8] Zhu Shanshan. Research on Machine Learning Evaluation Methods for Knee Joint Force in Sports. Journal of Lanzhou University of Arts and Science (Natural Science Edition), 2024(03), 100-104. [9] Sun Lin, Ma Zhongli, Chen Hao. Quantitative Analysis of Russia Sports Research in China Based on CNKI. Journal of Hebei Sports University, 2014, 28(03):30-33+63. [10] Mao Jie, Mei Yan. Application of Gray ART Clustering Analysis Method in Monitoring Biochemical Indicators of Competitive Sports. Journal of Wuhan Sports University, 2005(10):56-58.
Copyright @ 2020-2035 STEMM Institute Press All Rights Reserved