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)
Incoming Non-self Citations Over Time
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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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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 |
|---|---|---|---|---|
| 1,860 | Approximation Algorithms for Clustering Uncertain Data | 2008 | PODS | 0.0001028857 |
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