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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
3175
Venue
SIGMOD
Year
2000
Pagerank
6.3835403e-05
Overall Rank
4,177 | 70.95%
DOI
-

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Incoming Citations (Sorted by Pagerank)

Showing 5 of 5 citing papers.

Rank Citing Paper Year Venue Pagerank
2,404 Maintaining Variance and k–Medians over Data Stream Windows 2003 PODS 8.8837279e-05
2,789 Optimal Sampling from Sliding Windows 2009 PODS 8.1249652e-05
3,654 Using Trees to Depict a Forest 2009 VLDB 6.873144e-05
6,883 C2P: Clustering based on Closest Pairs 2001 VLDB 4.8960306e-05
7,759 Dscaler: Synthetically Scaling A Given Relational Database 2016 VLDB 4.6593145e-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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