MemStream: Memory-Based Streaming Anomaly Detection-Reference-Cited by-同舟云学术

MemStream: Memory-Based Streaming Anomaly Detection

Published:2022-04-25 Issue: Volume: Page:
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Container-title:Proceedings of the ACM Web Conference 2022
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Author:

Bhatia Siddharth¹,Jain Arjit²,Srivastava Shivin¹,Kawaguchi Kenji³,Hooi Bryan¹

Affiliation:

1. National University of Singapore, Singapore

2. IIT Bombay, India

3. Harvard University, USA

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3485447.3512221

Reference84 articles.

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3. Jinwon An and Sungzoon Cho. 2015. Variational autoencoder based anomaly detection using reconstruction probability. Special Lecture on IE(2015). Jinwon An and Sungzoon Cho. 2015. Variational autoencoder based anomaly detection using reconstruction probability. Special Lecture on IE(2015).

4. Fabrizio Angiulli and Fabio Fassetti. 2007. Detecting distance-based outliers in streams of data. In CIKM. Fabrizio Angiulli and Fabio Fassetti. 2007. Detecting distance-based outliers in streams of data. In CIKM.

5. Liang Bai , Xueqi Cheng , Jiye Liang , and Huawei Shen . 2016. An Optimization Model for Clustering Categorical Data Streams with Drifting Concepts. TKDE ( 2016 ). Liang Bai, Xueqi Cheng, Jiye Liang, and Huawei Shen. 2016. An Optimization Model for Clustering Categorical Data Streams with Drifting Concepts. TKDE (2016).

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