Authors: Ramaraju Chithra; Savarimuthu Nickolas
Addresses: Department of Computer Applications, National Institute of Technology, Tiruchirappalli-620015, India. ' Department of Computer Applications, National Institute of Technology, Tiruchirappalli-620015, India
Abstract: 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.
Keywords: high utility pattern mining; two-phase algorithms; high utility pattern trees; HUPT; HUP growth; data mining; pruning strategies.
International Journal of Business and Systems Research, 2012 Vol.6 No.3, pp.279 - 295
Received: 08 May 2021
Accepted: 12 May 2021
Published online: 15 Jul 2012 *