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Quality and Efficiency in Kernel Density Estimates for Large Data

Summary: Randomized and deterministic KDE algorithms with quality guarantees for huge data; no kernel or bandwidth knowledge required. Highly parallelizable, MapReduce-friendly; orders-of-magnitude efficiency gains with strong empirical validation on real data. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
4788
Venue
SIGMOD
Year
2013
Pagerank
8.3130624e-05
Overall Rank
2,638 | 81.91%
DOI
10.1145/2463676.2465319

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{zheng_sigmod13,
        title = {{Quality and Efficiency in Kernel Density Estimates for Large Data}},
        author = {Zheng, Yan and Jestes, Jeffrey and Phillips, Jeff M. and Li, Feifei},
        series = {{SIGMOD} '13},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/2463676.2465319},
        url = {https://dl.acm.org/doi/10.1145/2463676.2465319},
        year = {2013}
}

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