Feature Grouping-based Outlier Detection upon Streaming Trajectories (Extended abstract)

被引：0

作者：

Mao, Jiali ^{[1
]}

Wang, Tao ^{[1
]}

Jin, Cheqing ^{[1
]}

Zhou, Aoying ^{[1
]}

机构：

[1] East China Normal Univ, Sch Data Sci & Engn, Shanghai, Peoples R China

来源：

2018 IEEE 34TH INTERNATIONAL CONFERENCE ON DATA ENGINEERING (ICDE) | 2018年

关键词：

D O I：

10.1109/ICDE.2018.00230

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The existing detection techniques are not tailored to identify the outlier which is close to its neighbors according to some features, but behaves significantly distinct from its neighbors in terms of the other features. In this paper, we propose a feature grouping-based mechanism, and then present two algorithms to detect outliers (TF-outlier and MO-outlier) upon trajectory streams. The effectiveness and efficiency of our proposal are validated by the experiments on real trajectory data.

引用

页码：1745 / 1746

页数：2

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