Efficient strategies for incremental mining of frequent closed itemsets over data streams
作者：
Highlights：
• Mining closed itemsets over data streams for sliding window and landmark models.
• Intersection-based approach by novel data structure and pruning strategies.
• Insightful analysis and theoretical proof for handling the transaction deletion.
• Efficiency improvement by up to 2 orders of magnitude.
摘要
•Mining closed itemsets over data streams for sliding window and landmark models.•Intersection-based approach by novel data structure and pruning strategies.•Insightful analysis and theoretical proof for handling the transaction deletion.•Efficiency improvement by up to 2 orders of magnitude.
论文关键词：Data streams,Closed itemsets,Frequent itemsets,Data mining,Knowledge discovery
论文评审过程：Received 8 December 2020, Revised 3 August 2021, Accepted 9 November 2021, Available online 27 November 2021, Version of Record 3 December 2021.
论文官网地址：https://doi.org/10.1016/j.eswa.2021.116220
