Apriori算法改进及其在大学生心理健康测试的应用

Improved Apriori algorithm and its application in college students’ mental health test

针对大学生心理健康测试中知识发现的难点问题,本文基于Apriori改进算法研究关联规则挖掘,为心理健康测试系统开发提供支持。研究内容基于Apriori算法基本知识和Apriori算法流程,通过分析Apriori算法存在的多次重复扫描数据集和产生大量的候选项集等主要缺陷,从矩阵数据表示和运算、执行自连接操作、删除非频繁项集等三方面对算法进行改进。将改进算法应用于大学生心理健康测试数据挖掘并得到了预期结果。研究表明,Apriori改进算法减少了数据计算工作量,提高了挖掘效率。

In connection with some founded knowledge difficulties in college students psychological test,this paper provide support for mental health test system development that based on the Apriori Improved Algorithm for Mining Association Rules. The study contents in view of the basic knowledge of Apriori Algorithm and its process to improve algorithm,which from three aspects that including matrix data representation and operation,perform self-join operations and removing infrequent item sets by the ways of analyzing the major defects exist Apriori algorithm repeatedly scan data sets and produce a large number of candidate sets,etc... The improved algorithm is applied to students＇ psychological health test data mining and get the desired results. The study has shown that Apriori improved algorithm reduces the data to calculate the workload and improve the efficiency of mining.