中文摘要：

为提高不确定数据集上频繁模式挖掘的效率，针对已有算法在判断是否需要为头表中的某项创建子头表时的计算量比较大的问题，给出一个近似挖掘策略AAT．Mine，以损失小部分频繁项集为代价，提高整个算法的挖掘效率。采用三个不同的典型数据集进行了算法的测试，分别与目前最好的算法和典型算法进行性能对比。实验结果验证了近似算法AAT．Mine的时空效率都得到了提高。

英文摘要：

To improve the efficiency of frequent itemset mining upon uncertain dataset, addressing the issue of heavy computa- tion cost of existing algorithms on judging whether to build sub header table for a certain item in the header table, this paper proposed an approximation algorithm called AAT-Mine, at the cost of losing a small portion of frequent itemsets, improved the overall mining performance. It evaluated the AAT-Mine algorithm using three datasets against classical and state of art algo- rithms. Experimental results show that AAT-Mine not only outperforms AT-Mine, MBP, IMBP, UF-Growth and CUFP-Mine in terms of running time, but also remains efficient memory usage.