一种面向高校学生体测数据的模式挖掘方法

A Pattern Mining Method for College Students'Physical Fitness Test Data

目前,每年测试高校大学生的体质健康,会产生大量的数据,为了提高关联规则对体测数据的挖掘效率,提出了一种面向高校学生体质测试数据的模式挖掘方法。利用经典的关联规则挖掘方法如Apriori算法和频繁模式树(Frequent Pattern Tree,FP-Growth)算法,对体测数据进行关联规则挖掘。实验结果表明,该模式挖掘的最小数据集能有效提高关联规则算法对体测数据的模式挖掘效率。

At present,a large amount of data will be generated when testing the physical health of college students every year.In order to improve the efficiency of association rules in mining physical test data,a pattern mining method for college students'physical test data is proposed.The classical association rule mining methods such as Apriori algorithm and Frequent Pattern Tree(FP-Growth)algorithm are used to mine association rules from the body data.The experimental results show that the minimum data set of pattern mining can effectively improve the efficiency of the association rule algorithm in the pattern mining of physical data.