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Density Biased Sampling: An Improved Method for Data Mining and Clustering

Summary: Density biased sampling under-samples dense regions and over-samples sparse ones, preserving original densities with weighted samples. Single-pass, memory-efficient algorithm; uniform sampling is a special case, with up to 6× gains on Zipf-like clusters for mining and clustering. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
3237
Venue
SIGMOD
Year
2000
Pagerank
6.8227421e-05
Overall Rank
4,222 | 71.04%
DOI
10.1145/342009.335384

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{palmer_sigmod00,
        title = {{Density Biased Sampling: An Improved Method for Data Mining and Clustering}},
        author = {Palmer, Christopher R. and Faloutsos, Christos},
        series = {{SIGMOD} '00},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/342009.335384},
        url = {https://dl.acm.org/doi/10.1145/342009.335384},
        year = {2000}
}

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2,218 Maintaining Variance and k–Medians over Data Stream Windows 2003 PODS 8.9331834e-05
2,576 Optimal Sampling from Sliding Windows 2009 PODS 8.3964437e-05
3,881 Using Trees to Depict a Forest 2009 VLDB 7.0490549e-05
7,023 C2P: Clustering based on Closest Pairs 2001 VLDB 5.7242118e-05
7,866 Dscaler: Synthetically Scaling A Given Relational Database 2016 VLDB 5.5281143e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 8 of 8 cited papers.

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

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