PrePost+

In this paper, we propose a algorithm named PrePost+ for frequent itemset mining.PrePost+ employs N-lists to represent itemsets and Children-Parent Equivalence pruning to narrow search space.Experiment results on real datasets show that PrePost+ is effective and outperforms state-of-the-art algorithms. N-list is a novel data structure proposed in recent years. It has been proven to be very efficient for mining frequent itemsets. In this paper, we present PrePost+, a high-performance algorithm for mining frequent itemsets. It employs N-list to represent itemsets and directly discovers frequent itemsets using a set-enumeration search tree. Especially, it employs an efficient pruning strategy named Children-Parent Equivalence pruning to greatly reduce the search space. We have conducted extensive experiments to evaluate PrePost+ against three state-of-the-art algorithms, which are PrePost, FIN, and FP-growth¿, on six various real datasets. The experimental results show that PrePost+ is always the fastest one on all datasets. Moreover, PrePost+ also demonstrates good performance in terms of memory consumption since it use only a litter more memory than FP-growth¿ and less memory than PrePost and FIN.