K-Nearest Neighbor Search for Fuzzy Objects
Summary: Proposes two kNN variants for fuzzy objects: AKNN (probability-threshold) and RKNN (probability-range) over indeterministic boundaries. AKNN uses tighter distance approximations; RKNN applies strong pruning, with experiments on synthetic and real data. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Kai Zheng (University of Queensland)
- 2. Pui Cheong Fung (University of Queensland)
- 3. Xiaofang Zhou (National ICT Australia; University of Queensland)
BibTeX Citation
@inproceedings{zheng_sigmod10,
title = {{K-Nearest Neighbor Search for Fuzzy Objects}},
author = {Zheng, Kai and Fung, Pui Cheong and Zhou, Xiaofang},
series = {{SIGMOD} '10},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/1807167.1807243},
url = {https://dl.acm.org/doi/10.1145/1807167.1807243},
year = {2010}
}
Incoming Citations (Sorted by Pagerank)
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| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 6,217 | Clue-based Spatio-textual Query | 2017 | VLDB | 5.9425753e-05 |
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