Customized frequent patterns mining algorithms for enhanced Top-Rank-K frequent pattern mining

作者：

Highlights：

• Customizing general frequent pattern mining algorithms to efficient Top-Rank-K ones.

• Employing Dynamic Minimum Support Threshold Raising strategy to ensure efficiency.

• Outperforming BTK algorithm with a 90% runtime improvement.

• Experiments on real and synthetic datasets including Connect and Retail.

摘要

•Customizing general frequent pattern mining algorithms to efficient Top-Rank-K ones.•Employing Dynamic Minimum Support Threshold Raising strategy to ensure efficiency.•Outperforming BTK algorithm with a 90% runtime improvement.•Experiments on real and synthetic datasets including Connect and Retail.

论文关键词：Frequent patterns mining,Itemset rank,Top-Rank-K patterns,Data mining,Algorithm

论文评审过程：Received 2 February 2020, Revised 9 December 2020, Accepted 19 December 2020, Available online 24 December 2020, Version of Record 5 January 2021.

论文官网地址：https://doi.org/10.1016/j.eswa.2020.114530