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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)

Paper ID
6162
Venue
SIGMOD
Year
2021
Pagerank
4.2544238e-05
Overall Rank
9,924 | 30.97%
DOI
10.1145/3448016.3457271

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