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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
3986
Venue
SIGMOD
Year
2008
Pagerank
7.2224696e-05
Overall Rank
3,323 | 76.89%
DOI
-

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Incoming Citations (Sorted by Pagerank)

Showing 3 of 3 citing papers.

Rank Citing Paper Year Venue Pagerank
768 PathSim: Meta Path-Based Top-K Similarity Search in Heterogeneous Information Networks 2011 VLDB 0.00016919065
7,752 ABC: Attributed Bipartite Co-clustering 2022 VLDB 4.6601824e-05
12,176 Effective Data Co-Reduction for Multimedia Similarity Search 2011 SIGMOD 4.1945683e-05
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