HUPT-mine: an efficient algorithm for high utility pattern mining

In recent years, the problem of high utility pattern mining becomes one of the most important research areas in data mining. High utility pattern mining extracts patterns which have utility value higher than or equal to user specified minimum utility. The problem is challenging, because of the nonapplicability of anti-monotone property of frequent pattern mining. The existing high utility pattern mining algorithm adopts level wise candidate generation and many recently proposed approaches also generate large number of candidate itemsets. In this paper, a novel high utility pattern tree (HUPT) is proposed by applying two pruning strategies to reduce number of candidate itemsets by scanning database twice. For each conditional pattern base, a local tree is constructed with required information to generate candidate itemsets, by employing pattern growth approach. The experimental results on different datasets show that it reduces the number of candidate itemsets and also outperforms two-phase algorithm for dense datasets with long transactions.