Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Using association rules for product assortment decisions: a case study
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
FreeSpan: frequent pattern-projected sequential pattern mining
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
KDD-Cup 2000 organizers' report: peeling the onion
ACM SIGKDD Explorations Newsletter - Special issue on “Scalable data mining algorithms”
Is Sampling Useful in Data Mining? A Case in the Maintenance of Discovered Association Rules
Data Mining and Knowledge Discovery
Scalable Algorithms for Association Mining
IEEE Transactions on Knowledge and Data Engineering
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Sampling Large Databases for Association Rules
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Efficient mining of long frequent patterns from very large dense datasets
Design and application of hybrid intelligent systems
A fast high utility itemsets mining algorithm
UBDM '05 Proceedings of the 1st international workshop on Utility-based data mining
Mining itemset utilities from transaction databases
Data & Knowledge Engineering - Special issue: ER 2003
CTU-Mine: An Efficient High Utility Itemset Mining Algorithm Using the Pattern Growth Approach
CIT '07 Proceedings of the 7th IEEE International Conference on Computer and Information Technology
Building a more accurate classifier based on strong frequent patterns
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
Parallel Method for Mining High Utility Itemsets from Vertically Partitioned Distributed Databases
KES '09 Proceedings of the 13th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems: Part I
Efficient mining of high utility itemsets from large datasets
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
An efficient strategy for mining high utility itemsets
International Journal of Intelligent Information and Database Systems
High utility pattern mining using the maximal itemset property and lexicographic tree structures
Information Sciences: an International Journal
Mining high utility itemsets by dynamically pruning the tree structure
Applied Intelligence
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Mining High Utility Itemsets from a transaction database is to find itemsests that have utility above a user-specified threshold. This problem is an extension of Frequent Itemset Mining, which discovers itemsets that occur frequently (i.e. with occurrence count larger than a user given value). The problem of finding High Utility Itemsets is challenging, because the anti-monotone property so useful for pruning the search space in conventional Frequent Itemset Mining does not apply to it. In this paper we propose a new algorithm called CTU-PRO that mines high utility itemsets by bottom up traversal of a compressed utility pattern (CUP) tree. We have tested our algorithm on several sparse and dense data sets, comparing it with the recent algorithms for High Utility Itemset Mining and the results show that our algorithm works more efficiently.