Applying mutual information for discretization to support the discovery of rare-unusual association rule in cerebrovascular examination dataset
作者：
Highlights：
• A discretization approach based on mutual information concept was proposed.
• This research aims to extract RUARs in accordance with discretization result.
• A large dataset with about 12,000 medical data records is used to generate RUARs.
• Our MID method provides a high coverage ratio of RUARs.
• Graph-based visualization is used to represent RUARs clearly and easily.
摘要
•A discretization approach based on mutual information concept was proposed.•This research aims to extract RUARs in accordance with discretization result.•A large dataset with about 12,000 medical data records is used to generate RUARs.•Our MID method provides a high coverage ratio of RUARs.•Graph-based visualization is used to represent RUARs clearly and easily.
论文关键词：Rare-unusual association rules,Discretization,Apriori-Rare,Data mining,Cerebrovascular disease
论文评审过程：Received 23 March 2018, Revised 20 September 2018, Accepted 20 September 2018, Available online 21 September 2018, Version of Record 8 October 2018.
论文官网地址：https://doi.org/10.1016/j.eswa.2018.09.044
