基于事务映射区间求交的高效频繁模式挖掘算法

关联规则挖掘是数据挖掘重要研究课题，大数据处理对关联规则挖掘算法效率提出了更高要求，而关联规则挖掘的最耗时的步骤是频繁模式挖掘。针对当前频繁模式挖掘算法效率不高的问题，结合Apriori算法和FP-growth算法，提出一种基于事务映射区间求交的频繁模式挖掘算法IITM（interval interaction and transaction mapping），只需扫描数据集两次来生成FP树，然后扫描FP树将每个项的ID映射到区间中，通过区间求交来进行模式增长。该算法解决了Apriori算法需要多次扫描数据集，FP-growth算法需要迭代地生成条件FP树来进行模式增长而带来的效率下降的问题。在真实数据集上的实验显示，在不同的支持度下IITM算法都要要优于Apriori、FP-growth以及PIETM算法。

Efficient frequent pattern mining algorithm based on interval interaction and transaction mapping

Association rules mining is an important research topic in data mining. Big data processing puts forward higher requirements for the efficiency of association rules mining algorithm, where the most time consuming step is frequent pattern mining. For the problem that the state of art frequent pattern mining algorithm is not efficient, a frequent pattern mining algorithm based on interval interaction and transaction mapping (IITM) is proposed, which combines Apriori algorithm and FP-growth algorithm. This algorithm just needs to scan the dataset twice to generate the FP tree, and then scan the FP tree to map the ID of each transaction to the interval. It growths the frequent pattern by interval interaction and solves the problem that the Apriori algorithm needs to scan the dataset multiple times, the FP-growth algorithm needs to iterate to generate the conditional FP tree, which reduce the efficiency of the frequent pattern mining. Experiments on real dataset show that the IITM algorithm is superior to Apriori, FP-growth, and PIETM algorithms at different support.