On Saving Outliers for Better Clustering over Noisy Data
Summary: Outlier-saving: minimally adjust erroneous values to render outliers normal, enabling clustering on the cleaned data. NP-hardness proven; bounds, a guaranteed-approximation algorithm; experiments show improved clustering and downstream tasks. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Shaoxu Song (Tsinghua University)
- 2. Fei Gao (Tsinghua University)
- 3. Ruihong Huang (Tsinghua University)
- 4. Yihan Wang (Tsinghua University)
BibTeX Citation
@inproceedings{song_sigmod21,
title = {{On Saving Outliers for Better Clustering over Noisy Data}},
author = {Song, Shaoxu and Gao, Fei and Huang, Ruihong and Wang, Yihan},
series = {{SIGMOD} '21},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3448016.3457271},
url = {https://dl.acm.org/doi/10.1145/3448016.3457271},
year = {2021}
}
Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 8,492 | ShadowAQP: Efficient Approximate Group-by and Join Query via Attribute-oriented Sample Size Allocation and Data Generation | 2023 | VLDB | 5.4145838e-05 |
| 11,258 | Win-Win: On Simultaneous Clustering and Imputing over Incomplete Data | 2024 | VLDB | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 11 of 11 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 58 | The Merge/Purge Problem for Large Databases | 1995 | SIGMOD | 0.00040116748 |
| 112 | HoloClean: Holistic Data Repairs with Probabilistic Inference | 2017 | VLDB | 0.00032801121 |
| 291 | OPTICS: Ordering Points To Identify the Clustering Structure | 1999 | SIGMOD | 0.00022264197 |
| 376 | Discovering Denial Constraints | 2013 | VLDB | 0.00019677674 |
| 494 | Dependencies Revisited for Improving Data Quality | 2008 | PODS | 0.00017526549 |
| 549 | ERACER: A Database Approach for Statistical Inference and Data Cleaning | 2010 | SIGMOD | 0.00016692839 |
| 661 | Don’t be SCAREd: Use SCalable Automatic REpairing with Maximal Likelihood and Bounded Changes | 2013 | SIGMOD | 0.0001519162 |
| 693 | Algorithms for Mining Distance-Based Outliers in Large Datasets | 1998 | VLDB | 0.00014918477 |
| 700 | Explaining differences in multidimensional aggregates | 1999 | VLDB | 0.00014871032 |
| 955 | Truth Finding on the Deep Web: Is the Problem Solved? | 2013 | VLDB | 0.00012996675 |
| 2,660 | Finding Intensional Knowledge of Distance-Based Outliers | 1999 | VLDB | 8.2869029e-05 |
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| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
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| 2 | 2,340 | Online Outlier Detection in Sensor Data Using Non-Parametric Models | 2006 | VLDB |
| 3 | 6,205 | Outlier-robust Clustering using Independent Components | 2008 | SIGMOD |
| 4 | 9,952 | Distance-Based Outlier Detection: Consolidation and Renewed Bearing | 2010 | VLDB |
| 5 | 693 | Algorithms for Mining Distance-Based Outliers in Large Datasets | 1998 | VLDB |
| 6 | 8,070 | Towards Metric DBSCAN: Exact, Approximate, and Streaming Algorithms | 2024 | SIGMOD |
| 7 | 4,366 | Solving k-center Clustering (with Outliers) in MapReduce and Streaming, almost as Accurately as Sequentially | 2019 | VLDB |
| 8 | 578 | Efficient Algorithms for Mining Outliers from Large Data Sets | 2000 | SIGMOD |
| 9 | 10,176 | Clustering with Set Outliers and Applications in Relational Clustering | 2026 | PODS |
| 10 | 9,575 | Local Search Methods for k-Means with Outliers | 2017 | VLDB |