基于改进Apriori算法的评教系统应用研究

指出了Apriroi算法的不足,并提出改进算法:在剪枝侯选项目集的时候,采用生成频繁项目集和删除具有非频繁子集候选同时进行的方法来减少多余子项目集的生成。这样,既提高了扫描数据库过程中的信息获取率,又及时删除超集不是频繁项目集的项集,进一步缩减项集的潜在规模,提高了频繁项目集即关联规则生成的效率。讨论了如何利用改进的Apriori算法对评教系统数据进行关联规则挖掘。

Application and Research in Teaching Evaluation System Based on an Improved Apriori Algorithm

This paper analyses the lack of Apriori algorithm and presents an improved algorithm. This algorithm adopts a new method to reduce the redundant generation of sub-itemsets during pruning the candidate itemsets, which can form directly the set of frequent itemsets and eliminate candidates having a subset that is not frequent in the meantime. The result shows that it can raise the probability of obtaining information in scanning database and reduce the potential scale of itemsets. It is discussed how to apply an improved Apriori algorithm to exploring association rules of the data in Teaching Evaluation System.