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Traditional association rules mining cannot meet out the various demands arising from real world applications. A research area within utility based data mining is emerging world wide. It considers different values of individual items as utilities, and then mines the frequent utility itemsets. Utility mining focuses at incorporating utility considerations in data mining applications. In current research, all algorithms mining high utility itemsets are based on candidate set generation-and-test category where too many candidates will be generated and tested. In this paper, the authors are going to evaluate the performance of some of the popular utility mining algorithms, DHUI algorithm (Discover High Utility Item sets), Umining, and Fast Utility Mining (FUM).
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