HAOP-Miner: Self-adaptive high-average utility one-off sequential pattern mining

作者：

Highlights：

• Address self-adaptive HAOP mining which can discover extremely important patterns.

• We propose the HAOP-Miner algorithm that contains two key steps.

• HAOP-Miner employs an online Reverse filling strategy to calculate the support.

• HAOP-Miner adopts Apriori-like strategy to prune the candidate patterns.

• HAOP-Miner has a high level of efficiency and it is easier to find valuable patterns.

摘要

•Address self-adaptive HAOP mining which can discover extremely important patterns.•We propose the HAOP-Miner algorithm that contains two key steps.•HAOP-Miner employs an online Reverse filling strategy to calculate the support.•HAOP-Miner adopts Apriori-like strategy to prune the candidate patterns.•HAOP-Miner has a high level of efficiency and it is easier to find valuable patterns.

论文关键词：Sequential pattern mining,Self-adaptive,High average utility,Apriori property,One-off condition

论文评审过程：Received 25 October 2020, Revised 22 December 2020, Accepted 12 June 2021, Available online 27 June 2021, Version of Record 20 July 2021.

论文官网地址：https://doi.org/10.1016/j.eswa.2021.115449