关键词: 数据挖掘, Apriori算法, 大学生体测项目, 关联规则挖掘

Abstract: 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.

Key words: data mining, Apriori algorithm, undergraduate physical measurement project, association rule mining