Distributed mining of high utility time interval sequential patterns using mapreduce approach
作者：
Highlights：
• Proposed a MapReduce solution for mining high utility time interval sequential patterns.
• Time interval utility linked list data structure is designed for calculating the utility efficiently.
• Time interval sequence weighted downward closure property is introduced.
• Co-occurrence utility map data structure reduces the number of candidate sequences.
• Remaining utility upper bound and co-occurrence utility upper bound early prunes the candidates.
摘要
•Proposed a MapReduce solution for mining high utility time interval sequential patterns.•Time interval utility linked list data structure is designed for calculating the utility efficiently.•Time interval sequence weighted downward closure property is introduced.•Co-occurrence utility map data structure reduces the number of candidate sequences.•Remaining utility upper bound and co-occurrence utility upper bound early prunes the candidates.
论文关键词：Big data,High utility itemset mining,High utility sequential pattern mining,Time interval sequential pattern mining,Mapreduce framework
论文评审过程：Received 25 August 2018, Revised 31 August 2019, Accepted 19 September 2019, Available online 19 September 2019, Version of Record 26 September 2019.
论文官网地址：https://doi.org/10.1016/j.eswa.2019.112967
