Fuzzy Systems and Knowledge Discovery, Fourth International Conference on (2007)
Haikou, Hainan, China
Aug. 24, 2007 to Aug. 27, 2007
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/FSKD.2007.102
Fujiang Ao , National University of Defense
Yuejin Yan , National University of Defense
Jian Huang , National University of Defense
Kedi Huang , National University of Defense
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%.
J. Huang, K. Huang, F. Ao and Y. Yan, "A Novel Pruning Technique for Mining Maximal Frequent Itemsets," 2007 International Conference on Fuzzy Systems and Knowledge Discovery(FSKD), Haikou, 2007, pp. 469-473.