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
Pattern-growth methods for frequent pattern mining
Pattern-growth methods for frequent pattern mining
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
A bottom-up projection based algorithm for mining high utility itemsets
AIDM '07 Proceedings of the 2nd international workshop on Integrating artificial intelligence and data mining - Volume 84
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 top-K high utility itemsets
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
High utility pattern mining using the maximal itemset property and lexicographic tree structures
Information Sciences: an International Journal
Expert Systems with Applications: An International Journal
Mining high utility itemsets by dynamically pruning the tree structure
Applied Intelligence
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High utility itemsets mining extends frequent pattern mining to discover itemsets in a transaction database with utility values above a given threshold. However, mining high utility itemsets presents a greater challenge than frequent itemset mining, since high utility itemsets lack the anti-monotone property of frequent itemsets. Transaction Weighted Utility (TWU) proposed recently by researchers has anti-monotone property, but it is an overestimate of itemset utility and therefore leads to a larger search space. We propose an algorithm that uses TWU with pattern growth based on a compact utility pattern tree data structure. Our algorithm implements a parallel projection scheme to use disk storage when the main memory is inadequate for dealing with large datasets. Experimental evaluation shows that our algorithm is more efficient compared to previous algorithms and can mine larger datasets of both dense and sparse data containing long patterns.