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Construction and Analysis of Students’ Physical Health Portrait Based on Principal Component Analysis Improved Canopy-K-means Algorithm

Rongbiao Ji,Jianke Yang, Yehui Wu, Yadong Li, Rujia Li, Jiaojiao Chen,Jianping Yang

JOURNAL OF SUPERCOMPUTING(2024)

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摘要
With the advancement of society and the improvement of living standards, the significance of students’ physical health has become increasingly prominent. However, currently, the assessment and analysis of students’ physical health heavily depend on conventional statistical methods. Even with the application of data mining-related methodologies for analysis and evaluation, the exploitation and utilization of physical health big data remain relatively restricted. In this paper, an improved Canopy-K-means algorithm based on principal component analysis (PCA) is used to construct and analyze a portrait of students’ physical fitness and health. The method combines data dimensionality reduction techniques and cluster analysis techniques, and its combined performance is the best compared to other algorithms in the ablation experiments for both male and female student data groups. In this paper, the algorithm was used to process the grouping of physical fitness test data of male and female students, realize the construction and analysis of students’ physical fitness and health portrait, and give the exercise prescription for different categories of students. In this paper, the physical health test data of students of Yunnan Agricultural University in 2020–2022 were collected to carry out experiments, and the results found that there are differences in physical health status among students of different genders, grades, and majors in this university, and the physical health status of the students of Classes 2018 and 2019 is generally deficient; on different majors, the students of the Faculty of Agricultural Sciences are slightly superior to the Faculty of Science and Technology, and the students of the Faculty of Science and Technology are slightly superior to the students of the Faculty of Humanities and Social Sciences. Our study offers novel methods and ideas for the assessment and analysis of students’ physical health, holding significant implications for schools and related departments in formulating scientific and effective physical education policies and health promotion strategies.
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关键词
Improved Canopy-K-means algorithm,Principal component analysis,Data dimensionality reduction,Cluster analysis,Data mining techniques,Physical health portraits,Exercise prescription
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