改进的Apriori算法的研究与应用

Research and Application of Improved Apriori Algorithm

关联规则是数据挖掘领域研究的重要方面之一。其中,Apriori算法是关联规则的重要组成部分,针对Apriori算法在计算二项频繁集时易产生大量无效候选集的问题,以及对候选集进行冗余扫描的问题,在传统Apriori算法的基础上提出了一种改进的Apriori算法,即Apriori-L算法。改进的Apriori算法不仅能提高计算二项频繁集过程的时间效率,而且能加快整个算法的运算过程。将Apriori-L算法运用股市模块联动规则挖掘中,可推测出联动的股票模块,将改进后的算法应用到日常生活中为该算法的使用提供了依据,同时验证了Apriori-L算法的准确性。

Association rules are one of the most important aspects in the field of data mining. Among them,the Apriori algorithm is an important part of association rules,the Apriori algorithm is easy to produce in the calculation of two frequent itemsets when a large number of invalid candidate sets,and the problem of redundant scanning of candidate,based on the traditional Apriori algorithm,an improved Apriori algorithm is proposed,namely Apriori-L algorithm. The improved Apriori algorithm can not only improve the time efficiency of the calculation of two frequent sets,but also speed up the operation process of the whole algorithm. The Apriori-L algorithm can be used to excavate the stock market module interaction rules,and the linkage stock module can be guessed. The improved algorithm is applied to daily life,and provides the basis for the algorithm to be used. Meanwhile,it verifies the accuracy of the Apriori-L algorithm.