Discovering associations with numeric variables
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Extracting Share Frequent Itemsets with Infrequent Subsets
Data Mining and Knowledge Discovery
A Statistical Theory for Quantitative Association Rules
Journal of Intelligent Information Systems
Profit Mining: From Patterns to Actions
EDBT '02 Proceedings of the 8th International Conference on Extending Database Technology: Advances in Database Technology
An Efficient Algorithm for Mining Association Rules in Large Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in 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
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
Mining weighted association rules
Intelligent Data Analysis
Mining high utility itemsets in large high dimensional data
Proceedings of the 1st international conference on Forensic applications and techniques in telecommunications, information, and multimedia and workshop
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Although support has been used as a fundamental measure to determine the statistical importance of an itemset, it can't express other richer information such as quantity sold, unit profit, or other numerical attributes. To overcome the shortcoming, utility is used to measure the semantic importance and several algorithms for utility mining have been proposed. However, existing algorithms for utility mining adopt an Apriori-like candidate set generation-and-test approach and are inadequate on databases with long patterns. To solve the problem, this paper proposes a hybrid model and a novel algorithm, i.e., inter-transaction, to discover high utility itemsets from two directions: existing algorithms such as UMining [1] seeks short high utility itemsets from bottom, while inter-transaction seeks long high utility itemsets from top. To avoid the costly process of extending short itemsets step by step, inter-transaction find long itemsets directly by intersecting relevant transactions. Experiments on synthetic data show that the new algorithm achieves high performance, especially in high dimension data set.