High utility itemset mining with techniques for reducing overestimated utilities and pruning candidates

作者：

Highlights：

• MIQ-Tree structure for mining high utility itemsets is proposed.

• MU-Growth algorithm is suggested to prune candidates effectively in the mining process.

• Experimental results show that MU-Growth outperforms the other algorithms.

摘要

•MIQ-Tree structure for mining high utility itemsets is proposed.•MU-Growth algorithm is suggested to prune candidates effectively in the mining process.•Experimental results show that MU-Growth outperforms the other algorithms.

论文关键词：Candidate pruning,Data mining,High utility itemsets,Single-pass tree construction,Utility mining

论文评审过程：Available online 15 December 2013.

论文官网地址：https://doi.org/10.1016/j.eswa.2013.11.038