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Abstract
High-utility itemset mining (HUIM) is a critical issue in recent years since it can be used to reveal the profitable products by considering both the quantity and profit factors instead of frequent itemset mining (FIM) or association-rule mining (ARM). Several algorithms have been presented to mine high-utility itemsets (HUIs) and most of the designed algorithms have to handle the exponential search space for discovering HUIs when the number of distinct items and the size of database are very large. The proposed algorithm first adopts the TWU model to find the number of high-transaction-weighted utilization 1-itemsets (1-HTWUIs) as the particle size, which can greatly reduce the combinational problem in the evolution process Frequent weighted itemsets represent correlations frequently holding in data in which items may weight differently.