ZERO++: Harnessing the power of zero appearances to detect anomalies in large-scale data sets

• Main Authors: PANG, Guansong, TING, Kai Ming, ALBRECHT, David, JIN, Huidong
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2016
• Subjects: Artificial Intelligence and Robotics; Databases and Information Systems
• Online Access: https://ink.library.smu.edu.sg/sis_research/7026; https://ink.library.smu.edu.sg/context/sis_research/article/8029/viewcontent/11035_Article_Text_20550_1_10_20180216.pdf
• Institution: Singapore Management University

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 occ...