earticle

영어

High utility itemset mining algorithms should weigh itemset statistical information with their semantic importance to generate a more accurate and sensible description of the itemset utility. A novel High Utility Itemset tree (HUI-tree) structure, which is an extended prefix-tree structure for the storage of compressed utility information about itemsets, is proposed to address this issue. Moreover, FHUI-Growth, a fast approach for high utility itemset mining algorithm is developed for mining high utility itemsets. Mining efficiency is achieved with three new techniques: (1) both frequency statistic and complex itemset utility information can be compressed into the condensed HUI-tree structure, which successfully avoids multiple database rescan, (2) a part of the utility calculation process can be simplified because a tighter bottom bound pruning constrain can be obtained through the HUI-Tree, and (3) the costly tree scan operation is converted into the item conditional projection matrix row and column computation, which effectively reduces the mining process. Evaluations of the testing data set show that the execution performance and scalability are better than the classical Two-Phase algorithm.