Application of Improved K-Means Clustering Algorithm in Targeted Practice of College Volleyball Fitness Training Classification

The high-level men’s volleyball team in ordinary colleges and universities is an important place for the country to cultivate outstanding volleyball reserve talents. Especially under the background of “integration of sports and education,” the status of high-level men’s volleyball teams in ordinary...

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Bibliographic Details
Published in:Wireless communications and mobile computing Vol. 2022; no. 1
Main Author: Feng, Maodi
Format: Journal Article
Language:English
Published: Oxford Hindawi 2022
John Wiley & Sons, Inc
Subjects:
Algorithms
Classification
Cluster analysis
Clustering
Colleges & universities
Community colleges
Distance learning
Physical education teachers
Sports
Students
Support vector machines
Teaching
Teams
Training evaluation
User behavior
Vector quantization
Volleyball
Asia
ISSN:1530-8669, 1530-8677
Online Access:Get full text
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Summary:The high-level men’s volleyball team in ordinary colleges and universities is an important place for the country to cultivate outstanding volleyball reserve talents. Especially under the background of “integration of sports and education,” the status of high-level men’s volleyball teams in ordinary colleges and universities in my country’s volleyball career has been further improved. In affirmation and attention, routine training is an important part of the work of high-level men’s volleyball teams in ordinary colleges and universities. Cultivating high-level and high-quality outstanding volleyball reserve talents is an important goal of high-level men’s volleyball teams in ordinary colleges and universities. The growth of high-level men’s volleyball players requires high quality. For training support, in order to improve the training quality of the men’s volleyball team at the general college level, it is necessary to evaluate the training quality of the existing high-level sports teams. In order to achieve “different from person to person” and “teaching according to aptitude” college students’ volleyball training behavior prediction and guidance content recommendation, and to achieve a classification promotion strategy, this research uses the K-means clustering algorithm and PCA-GS-SVM algorithm to train college students’ volleyball training, adhering to behavior. The data classifier has high training efficiency, and the accuracy rate is higher than 87% in both the training set and the test set, which can meet the application requirements of college students’ volleyball training adhere to the classification and promotion strategy of behavior information platform and effectively support the practical application of the model. The volleyball training adherence behaviors are divided into eight categories, and specific classification promotion strategies are formulated according to the characteristics of different categories of behaviors.
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ISSN:1530-8669
1530-8677
DOI:10.1155/2022/9330013