【24h】

Association Rules based Data Mining on Test Data of Physical Health Standard

机译:基于关联规则的身体健康标准测试数据的数据挖掘

获取原文

摘要

With the development of modern electronic and computer technologies, sports training and competition became more and more technical. A great deal of data were recorded, including training data of athletes, test data of students in sports course, and test data of Physical Health Standard (PHS). However, usage of these records is limited to basic statistics analysis and only from some aspects of sport sciences. Important patterns of these datasets themselves and relationships among the data may still retain hidden. Data mining is a useful technique for finding unknown patterns among data, but it was seldom applied in sports field. In this paper, data mining attempt on test data of PHE is introduced, using Microsoft Association Rules algorithm and SQL Server 2005. In the experiment, the grades of vital capacity, grip strength, standing long jump and step test of a student are used for input attributes, and total score of the student is used for prediction attribute. As the results, we have a lot of useful rules and find that the grade of standing long jump is the most important influence factor on total score of a student.
机译:随着现代电子和计算机技术的发展,体育培训和竞争变得越来越能力。记录了大量数据,包括运动员的培训数据,体育过程中学生的测试数据,以及物理健康标准的测试数据(PHS)。然而,这些记录的使用仅限于基本统计分析,只有来自体育科学的某些方面。这些数据集本身的重要模式和数据之间的关系可能仍然可以保留隐藏。数据挖掘是一种有用的技术,用于查找数据之间的未知模式,但很少应用于运动场。在本文中,使用Microsoft关联规则算法和SQL Server 2005介绍了PHE测试数据的数据挖掘尝试。在实验中,使用了生命能力,握力,延长跳跃和学生的步骤测试的成绩输入属性和学生的总分数用于预测属性。结果,我们有很多有用的规则,并发现长期跳跃等级是学生总分数的最重要影响因素。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号