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Approximation Algorithms for Clustering Uncertain Data

Summary: Defines assigned vs unassigned models for clustering uncertain points and reduces uncertain k-means/k-median to weighted deterministic instances. Gives first approximation algorithms for uncertain k-center: O(k/ε · log^2 n) centers for (1+ε) and 2k centers for constant-factor. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
1459
Venue
PODS
Year
2008
Pagerank
0.00010287379
Overall Rank
1,863 | 87.06%
DOI
-

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

Rank Cited Paper Year Venue Pagerank
33 BIRCH: An Efficient Data Clustering Method for Very Large Databases 1996 SIGMOD 0.00077399244
103 ULDBs: Databases with Uncertainty and Lineage 2006 VLDB 0.00049520051
340 CURE: An Efficient Clustering Algorithm for Large Databases 1998 SIGMOD 0.00026854084
3,047 Sketching Probabilistic Data Streams 2007 SIGMOD 7.6537004e-05
3,391 Estimating Statistical Aggregates on Probabilistic Data Streams 2007 PODS 7.1427968e-05
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