关联规则挖掘中Apriori算法的研究与改进

Research and Improvement for Apriori Algorithm of Association Rule Mining

该文在对关联规则挖掘中Apriori算法的深入研究和分析的基础上，发现并指出了该算法存在的不足，并对其进行以下三方面改进：改善候选项集支持度的计算方法；缩小候选项集的生成规模；减少对数据库的扫描次数。实验结果表明．改进算法性能得到了明显提高。

On the basis of deep research and analysis of Apriori algorithm in association rule mining, the paper discovers some shortages of the algorithm, and then improves it from three aspects： Firstly, the calculation method of support in candidate frequent itemsets is improved; Secondly, the scale of candidate frequent itemsets is reduced; In the end, the numbers of scanned database are decreased. The experiment results of the improved algorithm show that the improved algorithm is more efficient than the original.