Efficient Algorithms for Mining High Utility Itemsets from Transactional Databases
National Cheng Kung University · University of Illinois Chicago
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 algorithms 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 two algorithms, namely utility pattern growth (UP-Growth) and UP-Growth+, for mining high utility itemsets with a set of effective strategies for…
Citation impact
- FWCI
- 52.90
- Percentile
- 100%
- References
- 49
Authors
4Topics & keywords
- Computer science
- Pruning
- Data mining
- Tree (set theory)
- Set (abstract data type)
- Database
- Database transaction
- Very large database