Title
Analysis Of Athletes' Stadium Stress Source Based On Improved Layered K-Means Algorithm
Abstract
Most of the research on stressors is in the medical field, and there are few analysis of athletes' stressors, so it can not provide reference for the analysis of athletes' stressors. Based on this, this study combines machine learning algorithms to analyze the pressure source of athletes' stadium. In terms of data collection, it is mainly obtained through questionnaire survey and interview form, and it is used as experimental data after passing the test. In order to improve the performance of the algorithm, this paper combines the known K-Means algorithm with the layering algorithm to form a new improved layered K-Means algorithm. At the same time, this paper analyzes the performance of the improved hierarchical K-Means algorithm through experimental comparison and compares the clustering results. In addition, the analysis system corresponding to the algorithm is constructed based on the actual situation, the algorithm is applied to practice, and the user preference model is constructed. Finally, this article helps athletes find stressors and find ways to reduce stressors through personalized recommendations. The research shows that the algorithm of this study is reliable and has certain practical effects and can provide theoretical reference for subsequent related research.
Year
DOI
Venue
2020
10.3233/JIFS-189065
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
Keywords
DocType
Volume
K-Means algorithm, athlete, stress source, machine learning
Journal
39
Issue
ISSN
Citations 
4
1064-1246
0
PageRank 
References 
Authors
0.34
0
1
Name
Order
Citations
PageRank
Gong Chen1588.46