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Uncertain Centroid based Partitional Clustering of Uncertain Data

Summary: Uncertain Centroid based Partitional Clustering treats the cluster centroid as a random variable derived from all deterministic representations of the uncertain objects. Theoretical and empirical results show improved clustering quality with comparable efficiency, addressing centroid limitations of prior partitional approaches. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
10505
Venue
VLDB
Year
2012
Pagerank
5.2415551e-05
Overall Rank
6,019 | 58.13%
DOI
-

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Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank Citing Paper Year Venue Pagerank
10,657 Stochastic SketchRefine: Scaling In-Database Decision-Making under Uncertainty to Millions of Tuples 2025 VLDB 4.1945683e-05
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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
1,860 Approximation Algorithms for Clustering Uncertain Data 2008 PODS 0.0001028857
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