Application of data mining techniques in sports training

Data mining techniques have been successfully applied in stock, insurance, medicine, banking and retailing domains. In the sport domain, for transforming sport data into actionable knowledge, coaches can use data mining techniques to plan training sessions more effectively, and to reduce the impact...

Full description

Saved in:
Bibliographic Details
Published in2012 5th International Conference on Biomedical Engineering and Informatics pp. 954 - 958
Main Authors Yingying Li, Yimin Zhang
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2012
Subjects
clustering techniques
data mining
physiological data
sports training
Online AccessGet full text

Cover

More Information
Summary:Data mining techniques have been successfully applied in stock, insurance, medicine, banking and retailing domains. In the sport domain, for transforming sport data into actionable knowledge, coaches can use data mining techniques to plan training sessions more effectively, and to reduce the impact of testing activity on athletes. This paper presents one such model, which uses clustering techniques, such as improved K-Means, Expectation-Maximization (EM), DBSCAN, COBWEB and hierarchical clustering approaches to analyze sport physiological data collected during incremental tests. Through analyzing the progress of a test session, the authors assign the tested athlete to a group of athletes and evaluate these groups to support the planning of training sessions.
ISBN:9781467311830
1467311839
DOI:10.1109/BMEI.2012.6513050