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Interactive Anomaly Detection
Keywords Interactive Anomaly Detection, Deviation Detection, Streaming Data, Data Mining, Machine Learning  +
Level Master  +
OneLineSummary Anomalies can be relevant or irrelevant to the end-user. The goal of this thesis is to propose a new interactive anomaly detection method to leverage the user-feedback and learn to suggest more relevant anomalies.  +
Prerequisites Requires very good understanding of ML and data mining techniques (especially for anomaly detection). Good programming skills (preferably in Python) are also required.  +
References - Lamba, H. and Akoglu, L., 2019, May. Lea … - Lamba, H. and Akoglu, L., 2019, May. Learning On-the-Job to Re-rank Anomalies from Top-1 Feedback. In Proceedings of the 2019 SIAM International Conference on Data Mining (SDM), pp. 612-620. Society for Industrial and Applied Mathematics. https://epubs.siam.org/doi/pdf/10.1137/1.9781611975673.69 - Ding, K., Li, J. and Liu, H., 2019, January. Interactive anomaly detection on attributed networks. In Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, pp. 357-365. ACM. http://www.public.asu.edu/~jundongl/paper/WSDM19_GraphUCB.pdf - Arnaldo, I., Veeramachaneni, K. and Lam, M., 2019. ex2: a framework for interactive anomaly detection. In ACM IUI Workshop on Exploratory Search and Interactive Data Analytics (ESIDA). http://ceur-ws.org/Vol-2327/IUI19WS-ESIDA-2.pdf - Zhu, Y. and Yang, K., 2019. Tripartite Active Learning for Interactive Anomaly Discovery. IEEE Access. https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8707963.ieee.org/stamp/stamp.jsp?arnumber=8707963
StudentProjectStatus Ongoing  +
Supervisors Mohamed-Rafik Bouguelia + , Onur Dikmen +
TimeFrame 4th of November 2019 to 29th May 2020  +
Title Interactive Anomaly Detection  +
Categories StudentProject  +
Modification dateThis property is a special property in this wiki. 8 September 2020 17:25:27  +
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