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A Framework for Clustering Uncertain Data

Summary: Clustering uncertain data via a general, model-driven framework; integrates visualization to quantify how uncertainty propagates into clustering outcomes. ELKI 0.7 implements diverse algorithms, distance measures, indexing, evaluation metrics, and visualization tools across multiple uncertainty models. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
11097
Venue
VLDB
Year
2015
Pagerank
5.3402052e-05
Overall Rank
5,755 | 59.97%
DOI
-

Incoming Non-self Citations Over Time

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Incoming Citations (Sorted by Pagerank)

Showing 4 of 4 citing papers.

Rank Citing Paper Year Venue Pagerank
2,126 MacroBase: Prioritizing Attention in Fast Data 2017 SIGMOD 9.4887794e-05
4,985 Pivot-based Metric Indexing 2017 VLDB 5.7856648e-05
9,025 Dimensional Testing for Reverse k-Nearest Neighbor Search 2017 VLDB 4.4072367e-05
11,219 F3 KM: Federated, Fair, and Fast k-means 2023 SIGMOD 4.1945683e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 3 of 3 cited papers.

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

Rank Cited Paper Year Venue Pagerank
101 ULDBs: Databases with Uncertainty and Lineage 2006 VLDB 0.0004955674
299 Trio: A System for Data, Uncertainty, and Lineage 2006 VLDB 0.00028525071
12,040 Interactive Data Mining with 3D-Parallel-Coordinate-Trees 2013 SIGMOD 4.1945683e-05
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