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- W2771797886 abstract "Traditional mining algorithms did not suit mining of global maximal frequent itemsets. Therefore, a new mining algorithm of global maximal frequent itemsets for health big data, namely, NMAGMFI algorithm was proposed. Firstly, the global frequent items were mined. Secondly, local FP-tree was reconstructed by each node. Thirdly, the mining results were combined by the center node. Finally, the global maximal frequent itemsets are mining by the strategy of top-down and FP-tree. Experimental results suggest that NMAGMFI algorithm is fast." @default.
- W2771797886 created "2017-12-22" @default.
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- W2771797886 date "2017-10-01" @default.
- W2771797886 modified "2023-09-25" @default.
- W2771797886 title "Research on mining global maximal frequent itemsets for health big data" @default.
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- W2771797886 doi "https://doi.org/10.1109/itoec.2017.8122534" @default.
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