A Shrinking-Based Approach for Multi-Dimensional Data Analysis
Summary: Introduces “shrinking,” a gravity-inspired preprocessing method that moves points along density gradients to form compact, separated clusters. Multi-scale grid connected-component detection plus cluster-wise evaluation enables efficient clustering in low- and high-dimensional spaces. (summarized by gpt-5.6-luna on Jul 24 2026)
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Authors
- 1. Yong Shi (State University of New York at Buffalo)
- 2. Yuqing Song (State University of New York at Buffalo)
- 3. Aidong Zhang (State University of New York at Buffalo)
BibTeX Citation
@article{shi_vldb03,
title = {{A Shrinking-Based Approach for Multi-Dimensional Data Analysis}},
author = {Shi, Yong and Song, Yuqing and Zhang, Aidong},
journal = {PVLDB},
series = {{VLDB} '03},
doi = {10.1016/B978-012722442-8/50046-X},
url = {https://doi.org/10.1016/B978-012722442-8/50046-X},
year = {2003}
}
Incoming Citations (Sorted by Pagerank)
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Outgoing Citations (Sorted by Pagerank)
Showing 9 of 9 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 32 | BIRCH: An Efficient Data Clustering Method for Very Large Databases | 1996 | SIGMOD | 0.00049714561 |
| 94 | Efficient and Effective Clustering Methods for Spatial Data Mining | 1994 | VLDB | 0.0003456395 |
| 300 | OPTICS: Ordering Points To Identify the Clustering Structure | 1999 | SIGMOD | 0.00021800242 |
| 308 | Automatic Subspace Clustering of High Dimensional Data for Data Mining Applications | 1998 | SIGMOD | 0.00021463972 |
| 364 | CURE: An Efficient Clustering Algorithm for Large Databases | 1998 | SIGMOD | 0.00019978187 |
| 826 | Optimal Multi-Step k-Nearest Neighbor Search | 1998 | SIGMOD | 0.000136317 |
| 1,305 | STING: A Statistical Information Grid Approach to Spatial Data Mining | 1997 | VLDB | 0.00011094431 |
| 1,679 | Fast Algorithms for Projected Clustering | 1999 | SIGMOD | 9.90334e-05 |
| 1,988 | WaveCluster: A Multi-Resolution Clustering Approach for Very Large Spatial Databases | 1998 | VLDB | 9.2470974e-05 |
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