Mining quantitative association rules in large relational tables
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Efficient mining of weighted association rules (WAR)
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Objective-Oriented Utility-Based Association Mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Mining Association Rules with Weighted Items
IDEAS '98 Proceedings of the 1998 International Symposium on Database Engineering & Applications
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Weighted Association Rule Mining using weighted support and significance framework
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
A fast high utility itemsets mining algorithm
UBDM '05 Proceedings of the 1st international workshop on Utility-based data mining
UP-Growth: an efficient algorithm for high utility itemset mining
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining high utility mobile sequential patterns in mobile commerce environments
DASFAA'11 Proceedings of the 16th international conference on Database systems for advanced applications - Volume Part I
Mining top-K high utility itemsets
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Distributed association rule mining with minimum communication overhead
AusDM '09 Proceedings of the Eighth Australasian Data Mining Conference - Volume 101
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Mining weighted association rules considers the profits of items in a transaction database, such that the association rules about important items can be discovered. However, high profit items may not always be high revenue products, since purchased quantities of items would also influence the revenue for the items. This paper considers both profits and purchased quantities of items to calculate utility for the items. Mining high utility quantitative association rules is to discover that when some items are purchased on some quantities, the other items on some quantities are purchased too, which have high utility. In this paper, we propose a data mining algorithm to find high utility itemsets with purchased quantities, from which high utility quantitative association rules also can be generated. Our algorithm needs not generate candidate itemsets and just need to scan the original database twice. The experimental results show that our algorithm is more efficient than the other algorithms which only discovered high utility association rules.