Exceeding Expectations and Clustering Uncertain Data

Summary: True approximation algorithms for k‑center clustering on probabilistic/uncertain data that preserve the number of centers, closing gaps in prior work. Introduce an "exceeding expectations" objective (contribution above expectation) and general optimization techniques under uncertainty. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID: 1498
Venue: PODS
Year: 2009
Pagerank: 5.7657129e-05
Overall Rank: 6,812 | 53.27%
DOI: 10.1145/1559795.1559836

Incoming Non-self Citations Over Time

Authors

1. Sudipto Guha (University of Pennsylvania)
2. Kamesh Munagala (Duke University)

BibTeX Citation

@inproceedings{guha_pods09,
        address = {New York, NY, USA},
        series = {{PODS} '09},
        title = {{Exceeding Expectations and Clustering Uncertain Data}},
        url = {https://dl.acm.org/doi/10.1145/1559795.1559836},
        doi = {10.1145/1559795.1559836},
        booktitle = {Proceedings of the {ACM} {SIGMOD} Symposium on {Principles} of {Database} {Systems}},
        publisher = {Association for Computing Machinery},
        author = {Guha, Sudipto and Munagala, Kamesh},
        year = {2009}
}

Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
8,741	Improvements on the k-center Problem for Uncertain Data	2018	PODS	5.3766157e-05
12,505	Large-Scale Uncertainty Management Systems: Learning and Exploiting Your Data (Tutorial Summary)	2009	SIGMOD	5.093636e-05

Outgoing Citations (Sorted by Pagerank)

Showing 1 of 1 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank	Cited Paper	Year	Venue	Pagerank
2,253	Approximation Algorithms for Clustering Uncertain Data	2008	PODS	8.8664016e-05

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