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

By Vincent Shin-Mu Tseng, Cheng-Wei Wu, Bai-En Shie, Philip S. Yu. Knowledge Discovery and Data Mining, 2010

560 citations recorded by the source corpus.

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.

Subfields: Computational Theory and Mathematics, Information Systems, Signal Processing

View Paper: https://www.semanticscholar.org/paper/a579bb390da6d2a164144a57a7b731cf0ab20d8b

How researchers see this paper

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In Computational Theory and Mathematics

1. Z. Zheng
2. Longbing Cao
3. Junfu Yin
4. Jun-Feng Qu
5. Mengchi Liu
6. Philippe Fournier-Viger

In Information Systems

1. Z. Zheng
2. Longbing Cao
3. Junfu Yin
4. Jun-Feng Qu
5. Mengchi Liu
6. Philippe Fournier-Viger

In Signal Processing

1. Z. Zheng
2. Longbing Cao
3. Junfu Yin
4. Jun-Feng Qu
5. Mengchi Liu
6. Philippe Fournier-Viger

Citations in the corpus

• Mining high utility itemsets without candidate generation (2012, methodology): “Like the performance evaluation of previous algorithms [5, 23, 22], external utilities for items are generated between 0.”
• USpan: an efficient algorithm for mining high utility sequential patterns (2012, methodology): “UP-Growth [12] also uses a tree structure, UP-Tree, to mine high utility patterns.”