[en] This paper proposes a simple yet effective anomaly detection method for multi-view data. The proposed approach detects anomalies by comparing the neighborhoods in different views. Specifically, clustering is performed separately in the different views and affinity vectors are derived for each object from the clustering results. Then, the anomalies are detected by comparing affinity vectors in the multiple views. An advantage of the proposed method over existing methods is that the tuning parameters can be determined effectively from the given data. Through experiments on synthetic and benchmark datasets, we show that the proposed method outperforms existing methods.
Disciplines :
Computer science
Author, co-author :
Marcos Alvarez, Alejandro ; Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation
Yamada, Makoto; Yahoo! Inc. > Yahoo! Labs > Search Science & Anti-Abuse Team
Kimura, Akisato; NTT Corporation > Media Information Laboratory > Media Recognition Research Group
Iwata, Tomoharu; NTT Corporation > Communication Science Laboratories > Learning and Intelligent Systems Research Group
Language :
English
Title :
Clustering-Based Anomaly Detection in Multi-View Data
Publication date :
October 2013
Event name :
ACM International Conference on Information and Knowledge Management (CIKM 2013)
Event organizer :
ACM
Event place :
San Francisco, United States
Event date :
from 27-10-2013 to 01-11-2013
Audience :
International
Main work title :
Proceedings of the 22nd International Conference on Information and Knowledge Management
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