Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Database Mining: A Performance Perspective
IEEE Transactions on Knowledge and Data Engineering
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
A fast high utility itemsets mining algorithm
UBDM '05 Proceedings of the 1st international workshop on Utility-based data mining
High-utility pattern mining: A method for discovery of high-utility item sets
Pattern Recognition
Mining high average-utility itemsets
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
An efficient strategy for mining high utility itemsets
International Journal of Intelligent Information and Database Systems
A tree-based approach for mining frequent weighted utility itemsets
ICCCI'12 Proceedings of the 4th international conference on Computational Collective Intelligence: technologies and applications - Volume Part I
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The average utility measure has recently been proposed to reveal a better utility effect of combining several items than the original utility measure. It is defined as the total utility of an itemset divided by its number of items within it. In this paper, a new mining approach with the aid of a tree structure is proposed to efficiently implement the concept. The high average utility pattern tree (HAUP tree) structure is first designed to help keep some related information and then the HAUP-growth algorithm is proposed to mine high average utility itemsets from the tree structure. Experimental results also show that the proposed approach has a better performance than the Apriori-like average utility mining.