基于C4.5算法的大学阳光体育系统设计与实现
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摘要
随着中国大学生规模的不断扩大,大学生的数量迅速增加,因此与学生相关的人体测量数据也显著增加,包括学生出勤信息和身体健康测试数据,因此做好数据管理工作是当前高校体育教育发展的必然要求。随着计算机技术的快速发展,数据挖掘技术已被应用于许多领域。本文从阳光体育开始,利用数据挖掘的C4.5算法构建基于学生真实体质健康测试数据的决策树,生成规则和知识。通过对决策树的分析,可以了解影响学生身体健康的重要因素与指标之间的关系,以指导高校适时调整体育教育的管理手段,从而全面提高学生的身体素质。
With the continuous expansion of the scale of Chinese college students, the number of college students has rapidly increased, so the number of anthropometric data related to students increase significantly, including student attendance information and physical health test data. Therefore, a good job in data management is an inevitable requirement for the development of physical education in colleges and universities. With the rapid development of computer technology, data mining technology is applies in many fields. This article starts from Sunshine Sports, uses the C4.5 algorithm of data mining to build a decision tree based on the student's real physique health test data, and generates rules and knowledge. Through the analysis of the decision tree, the relationship between important factors and indicators is abtained that affect students' physical health, in order to guide decision-makers timely adjustment of management tools, so as to comprehensively improve the students' physical fitness.
With the continuous expansion of the scale of Chinese college students, the number of college students has rapidly increased, so the number of anthropometric data related to students increase significantly, including student attendance information and physical health test data. Therefore, a good job in data management is an inevitable requirement for the development of physical education in colleges and universities. With the rapid development of computer technology, data mining technology is applies in many fields. This article starts from Sunshine Sports, uses the C4.5 algorithm of data mining to build a decision tree based on the student's real physique health test data, and generates rules and knowledge. Through the analysis of the decision tree, the relationship between important factors and indicators is abtained that affect students' physical health, in order to guide decision-makers timely adjustment of management tools, so as to comprehensively improve the students' physical fitness.
引文
[1]尹亚晶.基于数据挖掘的体育评价决策支持系统开发与研究[J].现代电子技术,2017,40(9):108-111.
[2]王倩.数据挖掘在阳光体育系统中的应用研究[D].天津:河北工业大学,2015.
[3]王宁涛.基于云计算的海量高校体育数据建模与分析[J].现代电子技术,2017,40(7):105-108.
[4]王杰,蔡良健,高瑜.一种基于决策树的多示例学习算法[J].郑州大学学报(理学版),2016,48(1):81-84.
[5]梁娟.多元化全民健身效益指数异构数据挖掘模型[J].科技通报,2015,31(8):108-110.
[6]杨正理,史文,陈海霞,王长鹏.大数据背景下高校招生策略预测[J/OL].智能系统学报,2019(2):1-7[2018-06-23].
[7]张斌,彭其渊,刘帆洨.基于并行C4.5的铁路零散白货客户流失预测研究[J/OL].计算机应用研究,2019(3):1-6[2018-06-23]
[8]杜景林,严蔚岚.基于距离权值的C4.5组合决策树算法[J].计算机工程与设计,2018,39(1):96-102.
[9]夏修臣,王秀英.基于余弦相似度的改进C4.5决策树算法[J].计算机工程与设计,2018,39(1):120-125.
[10]王蓉,刘遵仁,纪俊.基于属性重要度的决策树算法[J].计算机科学,2017,44(S2):129-132.
[11]王文霞.数据挖掘中改进的C4.5决策树分类算法[J].吉林大学学报(理学版),2017,55(5):1274-1277.
[12]董世彪.数据挖掘技术在少年健康体育行为应用中的研究[J].现代电子技术,2017,40(9):112-114,120.
[13]胡祖辉,施佺.高校学生上网行为分析与数据挖掘研究[J].中国远程教育,2017(2):26-32.
[14]成启明.基于决策树模型的计算机课程教学的学生属性影响分析[J].西南师范大学学报(自然科学版),2017,42(01):60-65.
[15]黄秀霞,孙力.C4.5算法的优化[J].计算机工程与设计,2016,37(5):1265-1270,1361.
[16]王大志,王贵良.体育训练数据管理规则挖掘ID3算法的属性增益优化[J].科技通报,2016,32(4):227-230.
[2]王倩.数据挖掘在阳光体育系统中的应用研究[D].天津:河北工业大学,2015.
[3]王宁涛.基于云计算的海量高校体育数据建模与分析[J].现代电子技术,2017,40(7):105-108.
[4]王杰,蔡良健,高瑜.一种基于决策树的多示例学习算法[J].郑州大学学报(理学版),2016,48(1):81-84.
[5]梁娟.多元化全民健身效益指数异构数据挖掘模型[J].科技通报,2015,31(8):108-110.
[6]杨正理,史文,陈海霞,王长鹏.大数据背景下高校招生策略预测[J/OL].智能系统学报,2019(2):1-7[2018-06-23].
[7]张斌,彭其渊,刘帆洨.基于并行C4.5的铁路零散白货客户流失预测研究[J/OL].计算机应用研究,2019(3):1-6[2018-06-23]
[8]杜景林,严蔚岚.基于距离权值的C4.5组合决策树算法[J].计算机工程与设计,2018,39(1):96-102.
[9]夏修臣,王秀英.基于余弦相似度的改进C4.5决策树算法[J].计算机工程与设计,2018,39(1):120-125.
[10]王蓉,刘遵仁,纪俊.基于属性重要度的决策树算法[J].计算机科学,2017,44(S2):129-132.
[11]王文霞.数据挖掘中改进的C4.5决策树分类算法[J].吉林大学学报(理学版),2017,55(5):1274-1277.
[12]董世彪.数据挖掘技术在少年健康体育行为应用中的研究[J].现代电子技术,2017,40(9):112-114,120.
[13]胡祖辉,施佺.高校学生上网行为分析与数据挖掘研究[J].中国远程教育,2017(2):26-32.
[14]成启明.基于决策树模型的计算机课程教学的学生属性影响分析[J].西南师范大学学报(自然科学版),2017,42(01):60-65.
[15]黄秀霞,孙力.C4.5算法的优化[J].计算机工程与设计,2016,37(5):1265-1270,1361.
[16]王大志,王贵良.体育训练数据管理规则挖掘ID3算法的属性增益优化[J].科技通报,2016,32(4):227-230.