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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
6224
Venue
SIGMOD
Year
2021
Pagerank
5.1648805e-05
Overall Rank
10,065 | 30.95%
DOI
10.1145/3448016.3457271

Incoming Non-self Citations Over Time

Authors

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}
}

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