中文摘要：

计算机应用为降低中医（TCM）方剂频繁模式挖掘过程中对经验参数的依赖，提高挖掘结果的准确性，针对中医方剂的数据特点，提出一种基于带权无向图的Top-Rank-k频繁模式挖掘算法。该算法可以直接挖掘出频繁k-itemset（kt〉3）而无需产生1-itemset和2-itemset，并随之快速回溯到核心药物组合的频繁项集所对应的方剂信息；此外，采用一种动态位向量（DBV）的压缩机制对无向图中边的权重进行压缩存储，以有效地提高算法的空间存储效率。分别对中医方剂数据集、真实数据集（Chess、Pumsb和Retail）和合成数据集（T1014D100K和Test2K50KDI）进行测试和比较．结果表明该算法与iNTK和BTK相比具有更高的时间和空间效率，而且也可以应用于其他类型的数据集。

英文摘要：

The dependency of the empirical parameters in frequent patterns mining of Traditional Chinese Medicine （TCM） prescriptions should be reduced to improve the accuracy of mining results. Aiming at the characteristics of TCM prescription data, an efficient Top-Rank-k frequent patterns mining algorithm based on Weighted Undirected Graph （WUG） was proposed. The new algorithm can directly mining frequent k-itemset （k≥3） without mining 1-times and 2-times, and then quikly backtrack to the corresponding prescription of the frequent itemsets of core drugs combination. Besides, the compression mechanism of Dynamic Bit Vector （DBV） was used to store the edge weights in undirected graph to improve the spatial storage efficiency of the algorithm. Experiments were conducted on TCM prescription datasets, real datasets （ Chess, Pumsb and Retail） and synthetic datasets （T1014D100K and Test2K50KD1）. The experimental results show that compared with iNTK （ improved Node-list Top-Rank-K） and BTK （ B-list Top-Rank-K）, the proposed algorithm has better performance in terms of time and space, and it can be applied to other types of data sets.