An efficient approach for outlier detection from uncertain data streams based on maximal frequent patterns

作者：

Highlights：

• We propose MFP-OD for outlier detection from uncertain data streams.

• We pay more attention to the associations between each data instance.

• We study the deviation index of each transaction and give the interpretation.

• Our method outperforms five baseline methods on four datasets.

摘要

•We propose MFP-OD for outlier detection from uncertain data streams.•We pay more attention to the associations between each data instance.•We study the deviation index of each transaction and give the interpretation.•Our method outperforms five baseline methods on four datasets.

论文关键词：Outlier detection,Maximal frequent pattern mining,Uncertain data streams,Deviation factors,Data streaming mining

论文评审过程：Received 14 November 2019, Revised 30 March 2020, Accepted 7 June 2020, Available online 20 June 2020, Version of Record 3 July 2020.

论文官网地址：https://doi.org/10.1016/j.eswa.2020.113646