Database Paper Browser

Back to papers

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
0.0001028857
Overall Rank
1,860 | 87.07%
DOI
-

Incoming Non-self Citations Over Time

Authors

Incoming Citations (Sorted by Pagerank)

Showing 6 of 6 citing papers.

Previous Page 1 / 1 Next

Outgoing Citations (Sorted by Pagerank)

Showing 5 of 5 cited papers.

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 0.00077324389
101 ULDBs: Databases with Uncertainty and Lineage 2006 VLDB 0.0004955674
341 CURE: An Efficient Clustering Algorithm for Large Databases 1998 SIGMOD 0.00026810548
3,041 Sketching Probabilistic Data Streams 2007 SIGMOD 7.6697078e-05
3,385 Estimating Statistical Aggregates on Probabilistic Data Streams 2007 PODS 7.1580391e-05
Previous Page 1 / 1 Next

Semantically Similar Papers