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)
Incoming Non-self Citations Over Time
Authors
Incoming Citations (Sorted by Pagerank)
Showing 7 of 7 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 2,118 | Using Probabilistic Models for Data Management in Acquisitional Environments | 2005 | CIDR | 9.5100739e-05 |
| 3,546 | Extracting Top-K Insights from Multi-dimensional Data | 2017 | SIGMOD | 6.9870745e-05 |
| 5,191 | Going Beyond Provenance: Explaining Query Answers with Pattern-based Counterbalances | 2019 | SIGMOD | 5.6378768e-05 |
| 5,987 | Sampling Cube: A Framework for Statistical OLAP Over Sampling Data | 2008 | SIGMOD | 5.2432535e-05 |
| 8,228 | Mining Approximate Top-K Subspace Anomalies in Multi-Dimensional Time-Series Data | 2007 | VLDB | 4.5549459e-05 |
| 9,420 | Local Search Methods for k-Means with Outliers | 2017 | VLDB | 4.3441378e-05 |
| 12,040 | Interactive Data Mining with 3D-Parallel-Coordinate-Trees | 2013 | SIGMOD | 4.1945683e-05 |
Previous
Page 1 / 1
Next
Outgoing Citations (Sorted by Pagerank)
Showing 13 of 13 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
Previous
Page 1 / 1
Next