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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)

Paper ID
3720
Venue
SIGMOD
Year
2005
Pagerank
5.4651579e-05
Overall Rank
8,215 | 43.64%
DOI
10.1145/1066157.1066211

Incoming Non-self Citations Over Time

Authors

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
6,205 Outlier-robust Clustering using Independent Components 2008 SIGMOD 5.9443504e-05
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Outgoing Citations (Sorted by Pagerank)

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

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