Outlier Detection Based on Group Behavior in Trajectory Data

DOI

10.2991/snce-18.2018.103How to use a DOI?

Keywords

Trajectory data; Exception object detection; Similarity calculation; Sliding Window; Hash table

Abstract

In recent years, with the rising popularity of GPS, and sensor network, the behavior of the trajectory data is collected and stored in the application server. There has been lots of research achievements at present in the application scenario. The uncertainty of the eventis not completely suitable for outlier detection algorithm of various kinds of application scenarios. In this paper, we study the behavior of the trajectory data, which is based on the behavior of the event and the group relation between objects, which based on the above two points. This paper introduce the object trajectory similarity computing the exception object detection algorithm.The experimental results on real datasets show the effectiveness and efficiency of proposed algorithms.

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Junling Liu
AU  - OwarayeAdemola Elijah
AU  - Dacheng Ye
PY  - 2018/05
DA  - 2018/05
TI  - Outlier Detection Based on Group Behavior in Trajectory Data
BT  - Proceedings of the 8th International Conference on Social Network, Communication and Education (SNCE 2018)
PB  - Atlantis Press
SP  - 509
EP  - 513
SN  - 2352-538X
UR  - https://doi.org/10.2991/snce-18.2018.103
DO  - 10.2991/snce-18.2018.103
ID  - Liu2018/05
ER  -