Abstract: Mining incessant itemsets is a fundamental issue in information mining and plays a significant job in numerous information mining applications. As of late, some itemset portrayals in light of hub sets have been proposed, which have demonstrated to be proficient for mining visit itemsets. In this paper, we propose a PrePost Computation Tree based Frequent Itemset Mining (PPCT-FIM), calculation for mining continuous itemsets. To accomplish high productivity, PPCT-FIM finds visit itemsets utilizing a set-list tree with a half breed search procedure and legitimately identifies visit itemsets without competitor age under some case. For assessing the exhibition of PPCT-FIM, we have direct broad examinations to contrast it against and existing driving calculations on an assortment of genuine and engineered datasets. The exploratory outcomes show that PPCT-FIM is altogether quicker than PFIM calculations.
Keywords: Data mining, Frequent itemset, Mining Massive Data, Pruning Rule, Incremental Update
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DOI:
10.17148/IJARCCE.2020.9127
