A Novel Structure to Mine High Utility Items Based on Transactional Weights

Association Rule Mining finds the frequent pattern rules in a dataset but association rule mining does not gives any information about the profit of the item. High utility itemset mining finds the high utility itemsets from the dataset, the high itemset discovers the profitable and useful patterns. Mining high utility itemset is challenging compared to mining association rule mining algorithm since the downward closure property is not satisfied in high utility itemset mining. Various state-of-the algorithms have been proposed to mine high utility itemset. In this paper a novel tree based data structure to mine high utility itemset is proposed and different pruning strategies is employed to effectively mine high utility itemset. The proposed idea is evaluated on 4 real time datasets. The result shows that the proposed algorithm performs better than the state-of –the-art algorithms in terms of memory and execution time.