Approximation Algorithms for Co-Clustering
Summary: First provable approximation algorithms for co-clustering: simple algorithms achieving constant-factor approximations for simultaneous row/column partitioning of matrices. Also prove co-clustering NP-hard, moving the problem from heuristics to rigorous algorithmic footing. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
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Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 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 2 of 2 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 4,187 | Clustering via Matrix Powering | 2004 | PODS | 6.3754336e-05 |
| 13,610 | Programmable Clustering | 2006 | PODS | - |
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