Fuzzy Clustering Prediction and Improvement Path Analysis of University Students’ Physique

DOI

10.2991/ahis.k.220601.019How to use a DOI?

Keywords

University Students; Physique; Fuzzy Clustering; Prediction; Promotion Path

Abstract

It is difficult for traditional methods to enter the fields of biology, psychology, medicine and social sciences because there are too many factors and complicated rules in these disciplines. Complexity and accuracy often exclude each other, but complexity is compatible with inaccuracy, namely fuzziness. Therefore, a new fuzzy clustering prediction method of university students’ physique is proposed, and the path of improving university students’ physique is analyzed. This paper introduces the five indicators of young students’ physique recognized by all countries in the world at present, and obtains the data structure of young students’ physique files. Data pre-processing is realized by calculating the average and standard deviation of sample data, standard deviation processing of sample data and normalization processing of sample data. The sample matrix and fuzzy compatibility matrix are established based on the fuzzy clustering of university students’ physique samples, and the fuzzy relation is further transferred equivalently. The transfer closure is obtained by matrix auto-multiplication method, and the appropriate cut-off set value is obtained. The similarity coefficients between the samples to be measured and each sample to be measured are calculated. The maximum similarity coefficients are found out and the classes of the samples to be measured are judged. By calculating the average indicator of each fuzzy clustering model, the fuzzy prediction analysis of university students’ physique can be realized in two stages. On the basis of the above analysis, universities should promote the physical health level of university students in an all-round way by improving the physical function of campus natural environment, improving the scientific nature of sports facilities, carrying out physical education reform, cultivating students’ scientific sports concept and good behavior habits. The experimental results show that the proposed method can effectively make clustering, and the purity and accuracy of clustering are high.

Copyright

© 2022 The Authors. Published by Atlantis Press International B.V.

Open Access

This is an open access article distributed under the CC BY-NC 4.0 license.

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TY  - CONF
AU  - Jinmao Tong
AU  - Fei Wang
PY  - 2022
DA  - 2022/06/02
TI  - Fuzzy Clustering Prediction and Improvement Path Analysis of University Students’ Physique
BT  - Proceedings of the 2021 International conference on Smart Technologies and Systems for Internet of Things (STS-IOT 2021)
PB  - Atlantis Press
SP  - 96
EP  - 100
SN  - 2589-4919
UR  - https://doi.org/10.2991/ahis.k.220601.019
DO  - 10.2991/ahis.k.220601.019
ID  - Tong2022
ER  -