Efficient mining of weighted association rules (WAR)
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Extracting Share Frequent Itemsets with Infrequent Subsets
Data Mining and Knowledge Discovery
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
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
Mining weighted association rules
Intelligent Data Analysis
Effective sanitization approaches to hide sensitive utility and frequent itemsets
Intelligent Data Analysis
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Traditional association rules mining (ARM) only concerns the frequency of itemsets, which may not bring large amount of profit. Utility mining only focuses on itemsets with high utilities, but the number of rich-enough customers is limited. To overcome the weakness of the two models, we propose a novel model, called general utility mining, which takes both frequency and utility into consideration simultaneously. By adjusting the weight of the frequency factor or the utility factor, this model can meet the different preferences of different applications. It is flexible and practicable in a broad range of applications. We evaluate our proposed model on a real-world database. Experimental results demonstrate that the mining results are valuable in business decision making.