Programmable Clustering
Summary: Introduce "programmable clustering": users specify acceptable clusterings via first-order logic formulas rather than an explicit objective. Show the general problem can be NP-hard but identify three FO specifications that admit efficient algorithms and give solutions. (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Sreenivas Gollapudi (Ebrary Inc.)
- 2. Ravi Kumar (Yahoo)
- 3. D. Sivakumar (Google; IBM)
BibTeX Citation
@inproceedings{gollapudi_pods06,
address = {New York, NY, USA},
series = {{PODS} '06},
title = {{Programmable Clustering}},
url = {https://dl.acm.org/doi/10.1145/1142351.1142400},
doi = {10.1145/1142351.1142400},
booktitle = {Proceedings of the {ACM} {SIGMOD} Symposium on {Principles} of {Database} {Systems}},
publisher = {Association for Computing Machinery},
author = {Gollapudi, Sreenivas and Kumar, Ravi and Sivakumar, D.},
year = {2006}
}
Incoming Citations (Sorted by Pagerank)
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| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 9,402 | Approximation Algorithms for Co-Clustering | 2008 | PODS | 5.2755515e-05 |
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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