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)
Incoming Non-self Citations Over Time
Authors
- 1. Feng Pan
- 2. Xiang Zhang
- 3. Wei Wang
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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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.
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