A novel approach for mining maximal frequent patterns

作者：

Highlights：

• An N-list structure is used to compress the dataset for mining Maximal Frequent Patterns.

• A pruning technique is then proposed and used in INLA-MFP algorithm to improve the runtime and memory usage.

• Experiments were conducted to show that INLA-MFP outperforms well-known algorithms.

摘要

•An N-list structure is used to compress the dataset for mining Maximal Frequent Patterns.•A pruning technique is then proposed and used in INLA-MFP algorithm to improve the runtime and memory usage.•Experiments were conducted to show that INLA-MFP outperforms well-known algorithms.

论文关键词：Data mining,Pattern mining,Maximal frequent patterns,N-list structure,Pruning technique

论文评审过程：Received 25 July 2016, Revised 16 December 2016, Accepted 17 December 2016, Available online 30 December 2016, Version of Record 6 January 2017.

论文官网地址：https://doi.org/10.1016/j.eswa.2016.12.023