Identifying risk factors for adverse diseases using dynamic rare association rule mining
作者：
Highlights：
• Devising a tree structure for efficient generation of complete set of patterns.
• Development of a single pass dynamic rare association rule mining algorithm.
• Evaluation of the algorithm based on transaction modification and threshold update.
• Analysis of risk factors for three clinical diseases using proposed approach.
• Comparison with existing approaches using synthetic and real-life datasets.
摘要
•Devising a tree structure for efficient generation of complete set of patterns.•Development of a single pass dynamic rare association rule mining algorithm.•Evaluation of the algorithm based on transaction modification and threshold update.•Analysis of risk factors for three clinical diseases using proposed approach.•Comparison with existing approaches using synthetic and real-life datasets.
论文关键词：Adverse diseases,Rare pattern,Association rule,Rare association rule,Dynamic databases
论文评审过程：Received 4 February 2018, Revised 12 June 2018, Accepted 2 July 2018, Available online 4 July 2018, Version of Record 20 July 2018.
论文官网地址：https://doi.org/10.1016/j.eswa.2018.07.010
