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NB-MAFIA: An N-List Based Maximal Frequent Itemset Algorithm
SHEN Gehui, LIU Peidong, DENG Zhihong
Acta Scientiarum Naturalium Universitatis Pekinensis    2016, 52 (2): 199-209.   DOI: 10.13209/j.0479-8023.2015.125
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Abstract The authors propose an efficient algorithm, NB-MAFIA, for mining maximal frequent itemset using NList, which uses node list of prefix tree to represent itemsets. By using N-List, itemsets’ support can be efficiently computed because of the high compactness of N-List and the efficiency of the method to intersect two N-Lists. Meanwhile, the authors employ some search space pruning strategies and superset checking strategy to improve NB-MAFIA. To evaluate NB-MAFIA, the authors compare proposed algorithm with two state-of-the-art algorithms on a variety of real and synthesis datasets. Experimental results show that NB-MAFIA is efficient and outperform the baseline algorithms in most case. Especially, NB-MAFIA is more efficient on dense datasets.

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