Hybrid data-driven outlier detection based on neighborhood information entropy and its developmental measures

作者：

Highlights：

• Neighborhood information entropy and its deep measures are built to detect outliers.

• The new outlier detection method applies to categorical, numeric, and mixed data.

• The NIEOD algorithm has better adaptability and effectiveness than six main ways.

• This study deepens current outlier detection from a new view of hybrid data-driving.

摘要

•Neighborhood information entropy and its deep measures are built to detect outliers.•The new outlier detection method applies to categorical, numeric, and mixed data.•The NIEOD algorithm has better adaptability and effectiveness than six main ways.•This study deepens current outlier detection from a new view of hybrid data-driving.

论文关键词：Outlier detection,Neighborhood rough set,Neighborhood information entropy,Hybrid data-driving,Data mining

论文评审过程：Received 14 September 2017, Revised 30 May 2018, Accepted 6 June 2018, Available online 7 June 2018, Version of Record 28 June 2018.

论文官网地址：https://doi.org/10.1016/j.eswa.2018.06.013