Multi-view group anomaly detection
conference contribution
posted on 2018-01-01, 00:00 authored by H Wang, P Su, M Zhao, Gang LiGang Li© 2018 Association for Computing Machinery. Multi-view anomaly detection is a challenging issue due to diverse data generation mechanisms and inconsistent cluster structures of different views. Existing methods of point anomaly detection are ineffective for scenarios where individual instances are normal, but their collective behavior as a group is abnormal. In this paper, we formalize this group anomaly detection issue, and propose a novel non-parametric bayesian model, named Multi-view Group Anomaly Detection (MGAD). By representing the multi-view data with different latent group and topic structures, MGAD first discovers the distribution of groups or topics in each view, then detects group anomalies effectively. In order to solve the proposed model, we conduct the collapsed Gibbs sampling algorithm for model inference. We evaluate our model on both synthetic and real-world datasets with different anomaly settings. The experimental results demonstrate the effectiveness of the proposed approach on detecting multi-view group anomalies.
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Pagination
277-286Location
Turin, ItalyPublisher DOI
Start date
2018-10-22End date
2018-10-26ISBN-13
9781450360142Language
engPublication classification
E1 Full written paper - refereedCopyright notice
2018, Association for Computing MachineryTitle of proceedings
CIKM 2018 : Proceedings of the 27th ACM International Conference on Information and Knowledge ManagementEvent
Information and Knowledge Management. ACM International Conference (27th : 2018 : Turin, Italy)Publisher
ACMPlace of publication
New York, N.Y.Usage metrics
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