Data Analysis of Soccer Athletes’ Physical Fitness Test Based on Multi-View Clustering
| Autor: | Bin Jiang, Yong Wang, Wanjian Bai, Wang Ning, Hongwei Xiong, Hongmei Li, Hua Sun |
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| Rok vydání: | 2018 |
| Předmět: |
History
biology Association rule learning business.industry Athletes Process (engineering) Computer science Association (object-oriented programming) Physical fitness biology.organism_classification Machine learning computer.software_genre Computer Science Applications Education Test (assessment) Artificial intelligence business Cluster analysis computer Test data |
| Zdroj: | Journal of Physics: Conference Series. 1060:012024 |
| ISSN: | 1742-6596 1742-6588 |
| DOI: | 10.1088/1742-6596/1060/1/012024 |
| Popis: | Data mining is a process that uses machine learning to extract valuable knowledge from a large number of data. The sportsman has a long period of exhaustion of human body motor function. The changes of physical function signals in each period are not obvious, there is a lack of dynamic association between the body function data, which makes the connection between the data not close. Traditional data mining methods are based on association rules mining technology [1], which excavates the most relevant data attributes in the data. Once the athletes' body function data appear more obvious faults, the correlation is weakened, which will cause the deep excavation of the disease is inaccurate. In this paper, we take athletes' different body function test data as different views of body performance, adopts a multi view clustering technology to analyse the association between test items, and further optimizes the physical fitness test index. The comprehensive evaluation of athletes based on physical data is transformed into a clustering problem, which can effectively solve the athletes' physical status evaluation. |
| Databáze: | OpenAIRE |
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