VEPRECO: Vertical databases with pre-pruning strategies and common candidate selection policies to fasten sequential pattern mining

作者：

Highlights：

• This work presents VEPRECO, a new efficient Sequential Pattern Mining algorithm.

• A new vertical representation of patterns is presented.

• New pre-pruning strategies are introduced.

• New common’s candidate selection policies are presented.

• VEPRECO reduces runtime and memory in datasets with multiple items in itemsets.

摘要

•This work presents VEPRECO, a new efficient Sequential Pattern Mining algorithm.•A new vertical representation of patterns is presented.•New pre-pruning strategies are introduced.•New common’s candidate selection policies are presented.•VEPRECO reduces runtime and memory in datasets with multiple items in itemsets.

论文关键词：Sequential pattern mining,Pruning strategies,Pattern representation,Pattern mining,Knowledge discovery

论文评审过程：Received 30 April 2021, Revised 12 April 2022, Accepted 4 May 2022, Available online 17 May 2022, Version of Record 23 May 2022.

论文官网地址：https://doi.org/10.1016/j.eswa.2022.117517