基于事务映射区间求交的高效频繁模式挖掘算法 postprint

Abstract: 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.