Analysing expertise through data mining: an example based on treading water

被引:6
|
作者
Schnitzler, Christophe [1 ]
Button, Chris [2 ]
Seifert, Ludovic [3 ]
Croft, James [2 ]
机构
[1] Univ Strasbourg, EA 2310, ESPE, LISEC, F-67000 Strasbourg, France
[2] Univ Otago, Sch Phys Educ Sport & Exercise Sci, Dunedin, New Zealand
[3] Univ Rouen, Fac Sports Sci, CETAPS UPRES EA 3832, Mont St Aignan, France
关键词
constraint-led approach; typology; data mining; machine learning; COORDINATION; PERFORMANCE; SPORT;
D O I
10.1080/02640414.2013.876085
中图分类号
G8 [体育];
学科分类号
04 ; 0403 ;
摘要
A classification system of treading water based on a conceptual typology was first established and then verified empirically. The typology was established on two concepts: the nature of the forces created within the water and the type of inter-limb coordination used. Thirty-eight participants were videotaped while treading water. Multivariate statistics were used to understand how the different behavioural types related to expertise. Three distinct groups of coordination patterns were adopted during treading water. A support vector machine procedure was used as a confirmatory procedure. The data mining process provides a methodological framework to analyse expertise in sports activities, and in this context suggests that a taxonomy can be established among the numerous coordination solutions that allow humans to create stabilising forces in the water.
引用
收藏
页码:1186 / 1195
页数:10
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