HOS-Miner: A System for Detecting Outlying Subspaces of High-dimensional Data

Summary: Introduces outlying subspaces: projections where sparse points become outliers, beyond fixed-space or full-dimensional detection. HOS-Miner dynamically searches subspaces, prioritizing locally sparse regions to efficiently find the strongest explanations. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID: 9303
Venue: VLDB
Year: 2004
Pagerank: -
Overall Rank: 13,958 | 4.57%
DOI: 10.1016/B978-012088469-8.50123-6

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Authors

1. Ji Zhang (University of Toronto)
2. Mong Li (University of Toronto)
3. Tok Wang Ling (University of Toronto)
4. Hui Wang (National University of Singapore)

BibTeX Citation

@article{zhang_vldb04,
        title = {{HOS-Miner: A System for Detecting Outlying Subspaces of High-dimensional Data}},
        author = {Zhang, Ji and Li, Mong and Ling, Tok Wang and Wang, Hui},
        journal = {PVLDB},
        series = {{VLDB} '04},
        pages = {265--276},
        doi = {10.1016/B978-012088469-8.50123-6},
        url = {https://doi.org/10.1016/B978-012088469-8.50123-6},
        year = {2004}
}

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