Guansong Pang, Kai Ming Ting, David Albrecht and Huidong Jin (2016) "ZERO++: Harnessing the Power of Zero Appearances to Detect Anomalies in Large-Scale Data Sets", Volume 57, pages 593-620

PDF | doi:10.1613/jair.5228

This paper introduces a new unsupervised anomaly detector called ZERO++ which employs the number of zero appearances in subspaces to detect anomalies in categorical data. It is unique in that it works in regions of subspaces that are not occupied by data; whereas existing methods work in regions occupied by data. ZERO++ examines only a small number of low dimensional subspaces to successfully identify anomalies. Unlike existing frequency-based algorithms, ZERO++ does not involve subspace pattern searching. We show that ZERO++ is better than or comparable with the state-of-the-art anomaly detection methods over a wide range of real-world categorical and numeric data sets; and it is efficient with linear time complexity and constant space complexity which make it a suitable candidate for large-scale data sets.

Click here to return to Volume 57 contents list