Pruning strategies for mining high utility itemsets

作者：

Highlights：

• Presents an efficient high utility mining method.

• Employs novel pruning strategies to limit the search space of utility mining.

• Compares the proposed method against a state-of-the-art utility mining method.

• Experimentally evaluates the system on eight real and synthetic benchmark datasets.

• Empirical results are found to be quite promising, especially for sparse transactional databases.

摘要

•Presents an efficient high utility mining method.•Employs novel pruning strategies to limit the search space of utility mining.•Compares the proposed method against a state-of-the-art utility mining method.•Experimentally evaluates the system on eight real and synthetic benchmark datasets.•Empirical results are found to be quite promising, especially for sparse transactional databases.

论文关键词：High utility itemsets,Frequent itemsets,Data mining

论文评审过程：Available online 8 November 2014.

论文官网地址：https://doi.org/10.1016/j.eswa.2014.11.001