This paper studies the fundamental problems of mining association rules. Based on the summary of classical mining algorithms and the inherent defects of Apriori algorithm, some related improvements are researched. Firstly, in order to avoid scanning the database repeatedly, the paper proposes a new method changing the database mapping way. Secondly, with the support of candidate item sets got, each candidate item set should be determined whether it is a frequent item set or not based on the prior knowledge of Apriori algorithm. If the candidate item sets generated by the element of the existing frequent item sets are certainly not frequent item sets, the element is not necessary to connect with others, avoiding producing lots of candidate items, which leads to an optimized connecting step. Lastly, for Apriori algorithm, the intersection operation is introduced to address the problems that it costs too much time to match candidate item sets with transaction patterns. Furthermore, to verify the effectiveness, the optimized algorithm has been applied to the hydrological historical data. The results of the experiments show that it costs shorter execution time under different supports and confident levels, gaining higher efficiency.
