UP-Growth: an efficient algorithm for high utility itemset mining

Authors:
Vincent S. Tseng;Cheng-Wei Wu;Bai-En Shie;Philip S. Yu
Affiliations:
Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan Roc;Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan Roc;Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan Roc;Department of Computer Science, University of Illinois at Chicago, Chicago, USA
Venue:
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Year:
2010

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Abstract

Mining high utility itemsets from a transactional database refers to the discovery of itemsets with high utility like profits. Although a number of relevant approaches have been proposed in recent years, they incur the problem of producing a large number of candidate itemsets for high utility itemsets. Such a large number of candidate itemsets degrades the mining performance in terms of execution time and space requirement. The situation may become worse when the database contains lots of long transactions or long high utility itemsets. In this paper, we propose an efficient algorithm, namely UP-Growth (Utility Pattern Growth), for mining high utility itemsets with a set of techniques for pruning candidate itemsets. The information of high utility itemsets is maintained in a special data structure named UP-Tree (Utility Pattern Tree) such that the candidate itemsets can be generated efficiently with only two scans of the database. The performance of UP-Growth was evaluated in comparison with the state-of-the-art algorithms on different types of datasets. The experimental results show that UP-Growth not only reduces the number of candidates effectively but also outperforms other algorithms substantially in terms of execution time, especially when the database contains lots of long transactions.