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
- 1. Charu C. Aggarwal (IBM)
- 2. Philip S. Yu (IBM)
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}
}
Incoming Citations (Sorted by Pagerank)
Showing 7 of 7 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 2,367 | Using Probabilistic Models for Data Management in Acquisitional Environments | 2005 | CIDR | 8.6855754e-05 |
| 3,132 | Extracting Top-K Insights from Multi-dimensional Data | 2017 | SIGMOD | 7.7246394e-05 |
| 4,638 | Going Beyond Provenance: Explaining Query Answers with Pattern-based Counterbalances | 2019 | SIGMOD | 6.5919801e-05 |
| 5,651 | Sampling Cube: A Framework for Statistical OLAP Over Sampling Data | 2008 | SIGMOD | 6.1336568e-05 |
| 8,229 | Mining Approximate Top-K Subspace Anomalies in Multi-Dimensional Time-Series Data | 2007 | VLDB | 5.4620216e-05 |
| 9,575 | Local Search Methods for k-Means with Outliers | 2017 | VLDB | 5.2528121e-05 |
| 12,238 | Interactive Data Mining with 3D-Parallel-Coordinate-Trees | 2013 | SIGMOD | 5.093636e-05 |
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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.
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