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Enhanced Association Rule Mining Algorithm to Extract High Utility Itemsets from a Large Dataset

K. Karpagam, Dr.R. Balasubramanian


Data mining aims at bringing out the hidden information from a large data set using data mining techniques according to the requirements. Association rule mining identifies itemsets that occur frequently in data set and frames association rules by taking all items equally. But many differences exist among the items that play a vital role in decision making. By taking one or more values of items as utilities, the utility mining technique works on finding the itemsets with greater utilities. In the proposed paper we present a utility mining algorithm named IUM (Improved Utility Mining) algorithm that finds high utility itemsets and also low utility itemsets from a large data set and the experiments states that the proposed algorithm performs better than existing algorithms in case of running time.


Association Rules, Frequent Itemsets, Low Utility Itemset, High Utility Itemset.

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