Anomaly Detection for Data Streams Based on Isolation Forest Using Scikit-Multiflow-Reference-Cited by-同舟云学术

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Anomaly Detection for Data Streams Based on Isolation Forest Using Scikit-Multiflow

Published:2020 Issue: Volume: Page:15-30
ISSN:0302-9743
Container-title:Computational Science and Its Applications – ICCSA 2020
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Author:

Togbe Maurras Ulbricht,Barry Mariam,Boly Aliou,Chabchoub Yousra,Chiky Raja,Montiel Jacob,Tran Vinh-Thuy

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-030-58811-3_2

Reference29 articles.

1. Aggarwal, C.C.: Outlier Analysis, 2nd edn. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-47578-3

2. Aggarwal, C.C., Han, J., Wang, J., Yu, P.S.: A framework for clustering evolving data streams. In: Proceedings of the 29th International Conference on Very Large Data Bases-Volume 29, pp. 81–92. VLDB Endowment (2003)

3. Angiulli, F., Fassetti, F.: Detecting distance-based outliers in streams of data. In: Proceedings of the Sixteenth ACM Conference on Information and Knowledge Management, pp. 811–820. ACM (2007)

4. Lecture Notes in Computer Science;I Assent,2012

5. Behera, R.K., Das, S., Jena, M., Rath, S.K., Sahoo, B.: A comparative study of distributed tools for analyzing streaming data. In: 2017 International Conference on Information Technology (ICIT), pp. 79–84 (2017)

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