What is the nearest neighbor in high dimensional spaces?
Summary: Redefines high-dimensional NN as a generalized problem by choosing query-specific projections. Projections are scored by how well data clusters around the query, enabling an efficient algorithm and revealing new insights into high-dimensional NN behavior. (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 14 of 14 citing papers.
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Outgoing Citations (Sorted by Pagerank)
Showing 7 of 7 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 2 | R-Trees: A Dynamic Index Structure For Spatial Searching | 1984 | SIGMOD | 0.0032118946 |
| 78 | A Quantitative Analysis and Performance Study for Similarity-Search Methods in High-Dimensional Spaces | 1998 | VLDB | 0.00056385781 |
| 129 | The X-tree: An Index Structure for High-Dimensional Data | 1996 | VLDB | 0.00044255276 |
| 665 | Fast Nearest Neighbor Search in Medical Image Databases | 1996 | VLDB | 0.00018437125 |
| 1,596 | Fast Algorithms for Projected Clustering | 1999 | SIGMOD | 0.00011210688 |
| 1,755 | Efficient User-Adaptable Similarity Search in Large Multimedia Databases | 1997 | VLDB | 0.00010661725 |
| 2,023 | Finding Generalized Projected Clusters in High Dimensional Spaces | 2000 | SIGMOD | 9.7586371e-05 |
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