挖掘关联规则中AprioriTid算法的改进

针对Apriori和AprioriTid算法中存在的项集生成瓶颈问题,提出了一种基于事务集压缩、候选项集压缩和支持度布尔矩阵的改进AprioriTid算法。该算法中通过删去不必比较的事务来有效缩减数据集;优化频繁项集的自连接方式来减少生成的候选项集个数;使用支持度布尔矩阵来加快候选项集的验证速度。实验结果表明改进算法确实能有效减少相关计算量,比已有算法执行效率明显提高,同时验证了该算法在旋转机械故障诊断中的有效性。

Improvement on AprioriTid algorithm of mining association rules

The efficiency of mining association rules is an important field of Knowledge Discovery in Databases.In this paper we have proposed an improved AprioriTid algorithm with transactions reduction,candidate itemsets reduction and support matrix to solve the bottleneck of itemsets generation.The highly efficient method described in this paper minimizes the database by deleting many transactions which need not be scanned.We also show a method to reduce the number of candidate itemsets by optimizing the join procedure of frequent itemsets and a support matrix method to accelerate the verification speed of candidate itemsets is put forward.To this end,the IAT algorithm for mining frequent itemsets,which is the improvement algorithm of AprioriTid,is designed in this article.The experiment results of the algorithm show that the improved algorithm can decrease related computation quantity in large scale and improve the efficiency of the algorithm.The simulation results of knowledge acquisition for fault diagnosis also show the validity of IAT algorithm.