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SBSC: A fast Self-tuned Bipartite proximity graph-based Spectral Clustering

Summary: SBSC: self-tuned, parameter-free bipartite graph for spectral clustering with locality sparsification, selecting O(sqrt(N)) representatives. Bi-means/K-means pick reps in O(N log N); local neighbor search yields an O(N)-sized graph and O(N(K^2+log N)) clustering time, with faster, higher-quality results on large data. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
7366
Venue
SIGMOD
Year
2025
Pagerank
5.093636e-05
Overall Rank
10,794 | 25.95%
DOI
10.1145/3725418

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BibTeX Citation

@inproceedings{khan_sigmod25,
        title = {{SBSC: A fast Self-tuned Bipartite proximity graph-based Spectral Clustering}},
        author = {Khan, Abdul Atif and Maheshwari, Rashmi and Akhter, Mohammad Maksood and Mohanty, Sraban Kumar},
        series = {{SIGMOD} '25},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3725418},
        url = {https://dl.acm.org/doi/10.1145/3725418},
        year = {2025}
}

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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
5,380 SCAR — Spectral Clustering Accelerated and Robustified 2022 VLDB 6.2397041e-05
6,956 A New Sparse Data Clustering Method Based On Frequent Items 2023 SIGMOD 5.7303405e-05
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