Unsupervised online detection and prediction of outliers in streams of sensor data-Reference-Cited by-同舟云学术

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Unsupervised online detection and prediction of outliers in streams of sensor data

Published:2019-06-03 Issue:3 Volume:9 Page:285-314
ISSN:2364-415X
Container-title:International Journal of Data Science and Analytics
language:en
Short-container-title:Int J Data Sci Anal

Author:

Reunanen Niko,Räty Tomi,Jokinen Juho J.,Hoyt Tyler,Culler David

Funder

Technical Research Centre of Finland

Publisher

Springer Science and Business Media LLC

Subject

Applied Mathematics,Computational Theory and Mathematics,Computer Science Applications,Modelling and Simulation,Information Systems

Link

http://link.springer.com/content/pdf/10.1007/s41060-019-00191-3.pdf

Reference94 articles.

1. Aggarwal, C.: On abnormality detection in spuriously populated data streams. In: Proceedings of the ACM SIAM Conference on Data Mining, pp. 80–91 (2005)

2. Aggarwal, C., Zhao, Y., Yu, P.: Outlier detection in graph streams. In: Proceedings of the 27th International Conference on Data Engineering, pp. 399–409 (2011)

3. Angiulli, F., Fassetti, F.: Distance-based outlier queries in data streams: the novel task and algorithms. Data Min. Knowl. Dis. 20(2), 290–324 (2010)

4. Angiulli, F., Pizzuti, C.: Fast outlier detection in high dimensional spaces. In: Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery, Springer-Verlag, London, UK, PKDD ’02, pp. 15–26 (2002)

5. Arya, S., Mount, D., Netanyahu, N., Silverman, R., Wu, A.: An optimal algorithm for approximate nearest neighbor searching fixed dimensions. J. ACM 45(6), 891–923 (1998)

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