CURLER: Finding and Visualizing Nonlinear Correlation Clusters
Summary: CURLER finds and visualizes nonlinear correlation clusters in subspaces of high-dimensional data. It defines co-sharing level to fuse clusters by proximity and orientation, enabling merging of nonlinear clusters and visualization. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Anthony K. H. Tung (National University of Singapore)
- 2. Xin Xu (National University of Singapore)
- 3. Beng Chin Ooi (National University of Singapore)
BibTeX Citation
@inproceedings{tung_sigmod05,
title = {{CURLER: Finding and Visualizing Nonlinear Correlation Clusters}},
author = {Tung, Anthony K. H. and Xu, Xin and Ooi, Beng Chin},
series = {{SIGMOD} '05},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/1066157.1066211},
url = {https://dl.acm.org/doi/10.1145/1066157.1066211},
year = {2005}
}
Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 6,336 | Outlier-robust Clustering using Independent Components | 2008 | SIGMOD | 5.8085039e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 7 of 7 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 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 |
| 1,679 | Fast Algorithms for Projected Clustering | 1999 | SIGMOD | 9.90334e-05 |
| 1,882 | Finding Generalized Projected Clusters in High Dimensional Spaces | 2000 | SIGMOD | 9.4359061e-05 |
| 3,618 | A Monte Carlo Algorithm for Fast Projective Clustering | 2002 | SIGMOD | 7.1525532e-05 |
| 3,743 | Optimal Grid-Clustering: Towards Breaking the Curse of Dimensionality in High-Dimensional Clustering | 1999 | VLDB | 7.0562784e-05 |
| 4,974 | Computing Clusters of Correlation Connected Objects | 2004 | SIGMOD | 6.3289112e-05 |
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