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
- 2. Fei Gao
- 3. Ruihong Huang
- 4. Yihan Wang
Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 9,621 | ShadowAQP: Efficient Approximate Group-by and Join Query via Attribute-oriented Sample Size Allocation and Data Generation | 2023 | VLDB | 4.3167167e-05 |
| 11,050 | Win-Win: On Simultaneous Clustering and Imputing over Incomplete Data | 2024 | VLDB | 4.1945683e-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.
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