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)
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Authors
- 1. Abdul Atif Khan (PDPM Indian Institute of Information Technology, Design and Manufacturing)
- 2. Rashmi Maheshwari (PDPM Indian Institute of Information Technology, Design and Manufacturing)
- 3. Mohammad Maksood Akhter (PDPM Indian Institute of Information Technology, Design and Manufacturing)
- 4. Sraban Kumar Mohanty (PDPM Indian Institute of Information Technology, Design and Manufacturing)
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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| 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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