ZERO++: Harnessing the power of zero appearances to detect anomalies in large-scale data sets
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...
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| Main Authors: | , , , |
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| Format: | text |
| Language: | English |
| Published: |
Institutional Knowledge at Singapore Management University
2016
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| Subjects: | |
| 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 |
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| Institution: | Singapore Management University |
| Language: | English |