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Outlier Detection for High Dimensional Data

Summary: High-dimensional outlier detection; proximity-based definitions lose meaning in sparse spaces. Projection-based techniques analyze data projections to reveal meaningful outliers, addressing sparsity-induced ambiguity in high-dimensional data. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
3317
Venue
SIGMOD
Year
2001
Pagerank
6.8319192e-05
Overall Rank
4,209 | 71.13%
DOI
10.1145/375663.375668

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{aggarwal_sigmod01,
        title = {{Outlier Detection for High Dimensional Data}},
        author = {Aggarwal, Charu C. and Yu, Philip S.},
        series = {{SIGMOD} '01},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/375663.375668},
        url = {https://dl.acm.org/doi/10.1145/375663.375668},
        year = {2001}
}

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