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
A new method for mining Frequent Weighted Itemsets based on WIT-trees
Expert Systems with Applications: An International Journal
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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Association rule mining (ARM) identifies frequent itemsets from databases and generates association rules by assuming that all items have the same significance and frequency of occurrence in a record i.e. their weight and utility is the same (weight=1 and utility=1) which is not always the case. However, items are actually different in many aspects in a number of real applications such as retail marketing, nutritional pattern mining etc. These differences between items may have a strong impact on decision making in many application unlike the use of standard ARM. Our framework, Weighted Utility ARM (WUARM), considers the varied significance and different frequency values of individual items as their weights and utilities. Thus, weighted utility mining focuses on identifying the itemsets with weighted utilities higher than the user specified weighted utility threshold. We conduct experiments on synthetic and real data sets using standard ARM, weighted ARM and Weighted Utility ARM (WUARM) and present analysis of the results.