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

Guansong Pang, Kai Ming Ting, David Albrecht, Huidong Jin

    Research output: Contribution to journalArticleResearchpeer-review

    11 Citations (Scopus)


    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 frequencybased 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.

    Original languageEnglish
    Pages (from-to)593-620
    Number of pages28
    JournalJournal of Artificial Intelligence Research
    Publication statusPublished - 1 Dec 2016

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