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Distance-based Outlier Detection in Data Streams

Summary: Systematic benchmarking of DODDS algorithms on identical streams and platforms; unsupervised, distribution-free distance-based outlier detection. Results: MCOD dominates across settings; Thresh LEAP competitive only in limited cases, highlighting MCOD’s time/space efficiency. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
11408
Venue
VLDB
Year
2016
Pagerank
0.00010367451
Overall Rank
1,557 | 89.32%
DOI
10.14778/2994509.2994516

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Authors

BibTeX Citation

@article{tran_vldb16,
        title = {{Distance-based Outlier Detection in Data Streams}},
        author = {Tran, Luan and Fan, Liyue and Shahabi, Cyrus},
        journal = {PVLDB},
        series = {{VLDB} '16},
        volume = {9},
        number = {12},
        pages = {1089--1100},
        doi = {10.14778/2994509.2994516},
        url = {https://doi.org/10.14778/2994509.2994516},
        year = {2016}
}

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