Clustering-based anomaly detection in multi-view data

Authors:
Alejandro Marcos Alvarez;Makoto Yamada;Akisato Kimura;Tomoharu Iwata
Affiliations:
University of Liege, Liege, Belgium;Yahoo! Labs, Sunnyvale, CA, USA;NTT Corporation, Atsugi, Japan;NTT Corporation, Keihanna, Japan
Venue:
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Year:
2013

Citing 5
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Abstract

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.