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HOS-Miner: A System for Detecting Outlying Subspaces of High-dimensional Data
Summary: Detecting outlying subspaces in high-dimensional data; HOS-Miner finds subspaces where points are anomalous in the subspace but normal in the full space. A fast mining/search pipeline ranks subspaces, scalable to dimensions, with experiments on data.
(summarized by gpt-5-nano on Feb 09 2026)
- Paper ID
- 9112
- Venue
- VLDB
- Year
- 2004
- Pagerank
- -
- Overall Rank
- 13,706 | 4.65%
- DOI
-
-
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