Apriori算法的改进及其在试卷分析系统中的应用

Improved Apriori Algorithm and Its Application in Test Paper Analysis System

Apriori算法是一种挖掘布尔型关联规则的典型算法。该算法在生成频繁项集时会有频繁的数据库扫描操作,并且在由低维频繁项集连接生成高维候选项集时,如果频繁项集维数过大,笛卡尔积后就会产生大量的候选项集,从而影响算法的效率。针对上述2个方面对Apriori算法进行改进,并将改进后的算法应用在试卷分析系统中。经过系统测试,改进后的算法具有较高的效率和较强的稳定性。

Apriori algorithm is a typical Boolean association rules mining algorithm. Apriori algorithm will scan database frequently when generating frequent item sets, and if low - dimensional candidate item sets are too large, after Descartes operation it will generate enormous high - dimensional frequent item sets . These two reasons will affect the efficiency of the Apriori algorithm. This paper improved the apriori algorithm on the two issues, and the improved algorithm in the paper are applied in test paper analysis system. After testing, the improved algorithm has higher efficiency and better stability.