C2P: Clustering based on Closest Pairs
Summary: C2P: clustering for large spatial databases using spatial access methods to locate closest pairs. Extensions enable scalable clustering for diverse shapes and outliers, blending hierarchical and graph-theoretic approaches; validated analytically and experimentally. (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 2 of 2 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 7,704 | Clustering Objects on a Spatial Network | 2004 | SIGMOD | 4.6681826e-05 |
| 11,209 | Closest Pairs Search Over Data Stream | 2023 | SIGMOD | 4.1905499e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 11 of 11 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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Semantically Similar Papers
| Overall Rank | Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 2,023 | Finding Generalized Projected Clusters in High Dimensional Spaces | 2000 | SIGMOD | 9.7586371e-05 |
| 2,088 | Capacity Constrained Assignment in Spatial Databases | 2008 | SIGMOD | 9.5707955e-05 |
| 4,818 | Clustering by Pattern Similarity in Large Data Sets | 2002 | SIGMOD | 5.8955574e-05 |
| 11,469 | Fast Density-Peaks Clustering: Multicore-based Parallelization Approach | 2021 | SIGMOD | 4.1905499e-05 |
| 2,248 | On Efficient Spatial Matching | 2007 | VLDB | 9.1958821e-05 |
| 8,687 | A Generalized Approach for Reducing Expensive Distance Calls for A Broad Class of Proximity Problems | 2021 | SIGMOD | 4.4621895e-05 |
| 11,481 | Fast Parallel Algorithms for Euclidean Minimum Spanning Tree and Hierarchical Spatial Clustering* | 2021 | SIGMOD | 4.1905499e-05 |
| 27 | Efficient and Effective Clustering Methods for Spatial Data Mining | 1994 | VLDB | 0.00080846213 |
| 7,704 | Clustering Objects on a Spatial Network | 2004 | SIGMOD | 4.6681826e-05 |
| 1,769 | Closest Pair Queries in Spatial Databases | 2000 | SIGMOD | 0.00010608375 |