Maximal frequent itemsets (MFIs) mining is important for many applications. To improve the performance of the MFI algorithms, the key is to use appropriate pruning techniques which can maximally reduce the searching space of the algorithm. In this paper, we present a novel pruning technique, subset equivalence pruning. To mining MFIs in data streams, we reconstruct the FPmax* algorithm to a single-pass algorithm, named FPmax*-DS. Subset equivalence pruning technique is added in FPmax*-DS. The experiments show that the pruning technique can efficiently reduce the searching space. Especially for some dense datasets, the size of searching space can be trimmed off by about 40%.
['Fujiang Ao', 'Yuejin Yan', 'Jian Huang', 'Kedi Huang']
A Novel Pruning Technique for Mining Maximal Frequent Itemsets