Abstract
Today, high utility itemsets mining is an important research issue in data mining because it considers the profit and quantity of items in each transaction. Most high utility itemsets algorithms such as UP-Growth [9], Udepth [5], Two-Phase [3], PB (Projection-Based) [8], ect. use TWU model (Transaction Weight Utility) for pruning candidates. However, the number of candidate itemsets generated in these algorithms is enormous. In this paper, we propose a new candidate weight utility model (CWU) and HP (High Projection) algorithm base on CWU to reduce the number of candidate itemsets. The experimental results show that the performance and number candidate of our algorithm is better than Two-Phase [3], PB [8].
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