In the highly developed information society, network data expand rapidly and much important information hide behind the surge of data. So it is necessary that analyze a large amounts of data. Apriori algorithm is a frequent item set algorithm for mining association rules. Its core idea is to excavate frequent item sets through two stages including generating candidate sets and closed down testing of plot. May generate a large number of candidate sets and may need to repeat scanning database are the two major drawbacks of Apriori algorithm. By eliminating unnecessary transmission of records in the database, the improved Apriori algorithm effectively reduces the time spent on I/O, greatly optimizes the efifciency of the algorithm, proves and gives the algorithm implementation thought. In this paper, an enhanced Apriori algorithm is proposed which takes less scanning time. It is achieved by eliminating the redundant generation of sub-items during pruning the candidate item sets. Both traditional and enhanced Apriori algorithms are compared and analyzed in this paper.%在当今这个信息极度发达的社会，网络数据急剧膨胀，激增的数据背后隐藏着许多重要的信息，所以对大量数据进行分析是必要的。Apriori算法是一种挖掘关联规则的频繁项集算法，其核心思想是通过候选集生成和情节的向下封闭检测两个阶段来挖掘频繁项集。可能产生大量的候选集，以及可能需要重复扫描数据库是Apriori算法的两大缺点。文中提出了一种需要更少的扫描时间的Apriori算法，在剪枝候选项集的同时也在消除冗余的子项集的产生。改进的Apriori算法通过消除数据库中不需要记录的传输有效减少了I/O所花费的时间，Apriori算法的效率得到了极大的优化。文章给出了算法实现思想及证明，并对传统的和改进的Apriori算法进行比较和分析。
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