%0 Journal Article %A WEN Kai %A YIN Yuan %A ZHANG Chang %A ZHENG Yunjun %T Maximal frequent itemset mining algorithm based on DiffNodeset structure %D 2018 %R 10.11772/j.issn.1001-9081.2018040913 %J Journal of Computer Applications %P 3438-3443 %V 38 %N 12 %X In data mining, mining maximum frequent itemsets instead of mining frequent itemsets can greatly improve the operating efficiency of system. The running time consumption of existing maximum frequent itemset mining algorithms is still very large. In order to solve the problem, a new DiffNodeset Maxmal Frequent Itemset Mining (DNMFIM) algorithm was proposed. Firstly, a new data structure DiffNodeset was adopted to realize the fast calculation of intersection and support degree. Secondly, the connection method with linear time complexity was adopted to reduce the complexity of connecting two DiffNodesets and avoid multiple invalid calculations. Then, the set-enumeration tree was adopted as the search space, and a variety of optimal pruning strategies were used to reduce the search space. Finally, the superset detection technology used in the MAximal Frequent Itemset Algorithm (MAFIA) algorithm was adopted to improve the accuracy of algorithm effectively. The experimental results show that, DNMFIM algorithm outperforms MAFIA and N-list based MAFIA (NB-MAFIA) in terms of time efficiency. The proposed algorithm has a good performance when mining the maximal frequent itemsets in different types of datasets. %U http://www.joca.cn/EN/10.11772/j.issn.1001-9081.2018040913