SCAR — Spectral Clustering Accelerated and Robustified

Summary: SCAR jointly addresses spectral clustering’s noise sensitivity and eigendecomposition cost. It iteratively separates cleansed/noisy components and uses Nyström acceleration, outperforming recent methods in speed and clustering quality on highly noisy data. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID: 12973
Venue: VLDB
Year: 2022
Pagerank: 6.2397041e-05
Overall Rank: 5,380 | 63.09%
DOI: 10.14778/3551793.3551850

Incoming Non-self Citations Over Time

Authors

1. Ellen Hohma (Technical University of Munich)
2. Christian M.M. Frey (Christian-Albrecht University of Kiel)
3. Anna Beer (Aarhus University)
4. Thomas Seidl (Ludwig Maximilian University of Munich)

BibTeX Citation

@article{hohma_vldb22,
        title = {{SCAR — Spectral Clustering Accelerated and Robustified}},
        author = {Hohma, Ellen and Frey, Christian M.M. and Beer, Anna and Seidl, Thomas},
        journal = {PVLDB},
        series = {{VLDB} '22},
        volume = {15},
        number = {11},
        pages = {3031--3044},
        doi = {10.14778/3551793.3551850},
        url = {https://doi.org/10.14778/3551793.3551850},
        year = {2022}
}

Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
10,794	SBSC: A fast Self-tuned Bipartite proximity graph-based Spectral Clustering	2025	SIGMOD	5.093636e-05
11,253	Ensemble Clustering based on Meta-Learning and Hyperparameter Optimization	2024	VLDB	5.093636e-05

Outgoing Citations (Sorted by Pagerank)

Showing 0 of 0 cited papers.

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

Rank	Cited Paper	Year	Venue	Pagerank

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