CRD: Fast Co-clustering on Large Datasets Utilizing Sampling-Based Matrix Decomposition

Summary: CRD: fast co-clustering on large datasets using sampling-based matrix decomposition. It achieves linear time in m+n and requires only partial data in memory, enabling out-of-core co-clustering with competitive accuracy on real and synthetic data. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: h43bd2e66459b157e
Venue: SIGMOD
Year: 2008
Pagerank: 6.8601606e-05
Overall Rank: 4,005 | 73.08%
DOI: 10.1145/1376616.1376637

Incoming Non-self Citations Over Time

Authors

1. Feng Pan (University of North Carolina)
2. Xiang Zhang (University of North Carolina)
3. Wei Wang (University of North Carolina)

BibTeX Citation

@inproceedings{pan_sigmod08,
        title = {{CRD: Fast Co-clustering on Large Datasets Utilizing Sampling-Based Matrix Decomposition}},
        author = {Pan, Feng and Zhang, Xiang and Wang, Wei},
        series = {{SIGMOD} '08},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/1376616.1376637},
        url = {https://dl.acm.org/doi/10.1145/1376616.1376637},
        year = {2008}
}

Incoming Citations (Sorted by Pagerank)

Showing 3 of 3 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
739	PathSim: Meta Path-Based Top-K Similarity Search in Heterogeneous Information Networks	2011	VLDB	0.00014333298
7,887	ABC: Attributed Bipartite Co-clustering	2022	VLDB	5.4333698e-05
12,662	Effective Data Co-Reduction for Multimedia Similarity Search	2011	SIGMOD	4.9793485e-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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