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NETS: Extremely Fast Outlier Detection from a Data Stream via Set-Based Processing

Summary: Set-based outlier detection on streams by grouping nearby points, enabling early identification and avoiding repetitive distance calculations across sliding windows. Two-level dimensional filtering scales to high dimensions, delivering 5–25x faster processing than state-of-the-art with comparable memory. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
12014
Venue
VLDB
Year
2019
Pagerank
8.1604054e-05
Overall Rank
2,756 | 81.10%
DOI
10.14778/3342263.3342269

Incoming Non-self Citations Over Time

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BibTeX Citation

@article{yoon_vldb19,
        title = {{NETS: Extremely Fast Outlier Detection from a Data Stream via Set-Based Processing}},
        author = {Yoon, Susik and Lee, Jae-Gil and Lee, Byung Suk},
        journal = {PVLDB},
        series = {{VLDB} '19},
        volume = {12},
        number = {11},
        pages = {1303--1315},
        doi = {10.14778/3342263.3342269},
        url = {https://doi.org/10.14778/3342263.3342269},
        year = {2019}
}

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