Back to papers
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
- 9113
- Venue
- VLDB
- Year
- 2004
- Pagerank
- -
- Overall Rank
- 13,720 | 4.65%
- DOI
-
-
Incoming Non-self Citations Over Time
No non-self incoming citations found for this paper in this database.
Incoming Citations (Sorted by Pagerank)
Showing 0 of 0 citing papers.
| Rank |
Citing Paper |
Year |
Venue |
Pagerank |
Outgoing Citations (Sorted by Pagerank)
Showing 0 of 0 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank |
Cited Paper |
Year |
Venue |
Pagerank |
Semantically Similar Papers
| Overall Rank |
Paper |
Year |
Venue |
Pagerank |
| 5,919 |
Hierarchical Subspace Sampling: A Unified Framework for High Dimensional Data Reduction, Selectivity Estimation and Nearest Neighbor Search |
2002 |
SIGMOD |
6.1045823e-05 |
| 1,809 |
Finding Generalized Projected Clusters in High Dimensional Spaces |
2000 |
SIGMOD |
9.8035109e-05 |
| 9,421 |
Local Search Methods for k-Means with Outliers |
2017 |
VLDB |
5.3341661e-05 |
| 3,999 |
Continuous Outlier Detection in Data Streams: An Extensible Framework and State-Of-The-Art Algorithms |
2013 |
SIGMOD |
7.0402912e-05 |
| 623 |
Efficient Algorithms for Mining Outliers from Large Data Sets |
2000 |
SIGMOD |
0.00015752968 |
| 698 |
Algorithms for Mining Distance-Based Outliers in Large Datasets |
1998 |
VLDB |
0.00015007291 |
| 12,417 |
Detecting Clusters in Moderate-to-High Dimensional Data: Subspace Clustering, Pattern-based Clustering, and Correlation Clustering |
2008 |
VLDB |
5.1725247e-05 |
| 11,470 |
Fast and Exact Outlier Detection in Metric Spaces: A Proximity Graph-based Approach |
2021 |
SIGMOD |
5.1725247e-05 |
| 8,096 |
Mining Approximate Top-K Subspace Anomalies in Multi-Dimensional Time-Series Data |
2007 |
VLDB |
5.5466158e-05 |
| 4,142 |
Outlier Detection for High Dimensional Data |
2001 |
SIGMOD |
6.938149e-05 |