基于Apriori算法的大学生体测项目关联规则挖掘  

Association Rule Mining of Undergraduate Physical Test Items Based on Apriori Algorithm

作　　者：王劭华[1] 欧阳会丹 孙丹 王康 吴鸿萍 钟询 褚兴平 杨松涛[1] WANG Shao-hua;OUYANG Hui-dan;SUN Dan;WANG Kang;WU Hong-ping;ZHONG Xun;CHU Xing-ping;YANG Song-tao(College of Physical Education and Health,Jiangxi University of Traditional Chinese Medicine,Nanchang 330004,China;School of Software,Jiangxi Agricultural University,Nanchang 330045,China;Jiangxi Science and Technology Infrastructure Center,Nanchang 330003,China)

机构地区：[1]江西中医药大学体育健康学院,江西南昌330004 [2]江西农业大学软件学院,江西南昌330045 [3]江西省科技基础条件平台中心,江西南昌330003

出　　处：《计算机与现代化》2023年第3期66-70,78,共6页Computer and Modernization

基　　金：国家社科基金资助项目(16BTY061);江西省03专项及5G项目(20204ABC03A26)。

摘　　要：大学生身体素质是力量、速度、耐力、柔韧和灵敏的综合表现,为了测量大学生的身体素质情况并分析改善大学生的身体素质,本文分别测量了大学生中男生的各项指标(引体向上、50 m、1000 m、坐位体前屈和跳远)和女生的各项指标(一分钟仰卧起坐、50 m、800 m、坐位体前屈和跳远)。本文使用Apriori算法分别做了3组实验,即在最小支持度为50%和最小置信度为70%,最小支持度为60%和最小置信度为70%,最小支持度为70%和最小置信度为70%的前提下对某大学近5年的男生和女生的各项指标进行关联规则挖掘。实验结果表明:体重正常的学生每项体测分别及格的最小置信度都为70%以上,在所有关联项结果中最高的最小置信度为肺活量及格与体重正常的最小置信度为87.7%,而体重异常的学生体测分别及格的最小置信度都不大于70%。在所有关联项结果中身高与各项体测之间的最小置信度差异不明显。这验证了Apriori算法在大学生身体素质关联规则挖掘中发挥着重要的作用,利用挖掘出来的频繁项集,能够很好地辅助各大高校改善大学生的身体素质。Physical quality of college students is the comprehensive performance of strength,speed,endurance,flexibility and agility.In order to measure the physical quality of college students and analyze the improvement of their physical quality,the various indexes of male students(pull-up,50 m,1000 m,sitting forward and long jump)and the various indexs of female students(one-minute sit-up,50 m,800 m,sitting forward and long jump)are measured.In this paper,Apriori algorithm is used to carry out three groups of experiments respectively,namely under the premise of support of 50%and confidence of 70%,support of 60%and confidence of 70%,support of 70%and confidence of 70%,association rule mining is carried out on various indicators of male and female students in a certain university in recent five years.The experimental results show that the confidence of normal weight students to pass each body test is more than 70%,and the highest confidence of all related items is 87.7%for passing vital capacity and normal weight,while the confidence of abnormal weight students to pass each body test is less than 70%.There is no significant difference in confidence between height and body measurements among all related items,which verifies that Apriori algorithm plays an important role in the mining of association rules of college students’physical fitness.Using the mined frequent item set,it can well assist universities to improve the physical fitness of college students.

关 键 词：数据挖掘 APRIORI算法 大学生体测项目 关联规则挖掘 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]