Trajectory outlier detection approach based on common slices sub-sequence-Reference-Cited by-同舟云学术

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Trajectory outlier detection approach based on common slices sub-sequence

Published:2017-12-18 Issue:9 Volume:48 Page:2661-2680
ISSN:0924-669X
Container-title:Applied Intelligence
language:en
Short-container-title:Appl Intell

Author:

Yu Qingying,Luo Yonglong,Chen Chuanming,Wang Xiaohan

Funder

National Natural Science Foundation of China

Key Program for University Top Talents of Anhui Province

Natural Science Foundation of Anhui Province (CN)

University Natural Science Research Program of Anhui Province

Science and Technology Project of Wuhu City

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence

Link

http://link.springer.com/article/10.1007/s10489-017-1104-z/fulltext.html

Reference35 articles.

1. Lee JG, Han J, Li X (2008) Trajectory outlier detection: a partition-and-detect framework. In: Proceedings of the 24th IEEE International Conference on Data Engineering (ICDE), pp 140–149

2. Su H, Zheng K, Huang J et al (2015) Calibrating trajectory data for spatio-temporal similarity analysis. VLDB J 24(1):93–116

3. Sanchez I, Aye ZMM, Rubinstein BIP et al (2016) Fast trajectory clustering using hashing methods. In: Proceedings of the IEEE International Joint Conference on Neural Networks (IJCNN), pp 3689–3696

4. Anagnostopoulos C, Hadjiefthymiades S (2014) Intelligent trajectory classification for improved movement prediction. IEEE Trans Syst Man Cybern Syst 44(10):1301–1314

5. Chen CM, Pi DC, Fang ZR (2012) Artificial immune algorithm applied to short-term prediction for mobile object location. Electron Lett 48(17):1061–1062

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