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: 3659
Venue: SIGMOD
Year: 2005
Pagerank: 4.6355641e-05
Overall Rank: 7,835 | 45.55%
DOI: -

Incoming Non-self Citations Over Time

Authors

Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
5,768	Outlier-robust Clustering using Independent Components	2008	SIGMOD	5.3332593e-05

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
263	OPTICS: Ordering Points To Identify the Clustering Structure	1999	SIGMOD	0.00029955858
277	Automatic Subspace Clustering of High Dimensional Data for Data Mining Applications	1998	SIGMOD	0.00029356968
1,596	Fast Algorithms for Projected Clustering	1999	SIGMOD	0.00011210688
2,023	Finding Generalized Projected Clusters in High Dimensional Spaces	2000	SIGMOD	9.7586371e-05
3,381	A Monte Carlo Algorithm for Fast Projective Clustering	2002	SIGMOD	7.1538392e-05
3,477	Optimal Grid-Clustering: Towards Breaking the Curse of Dimensionality in High-Dimensional Clustering	1999	VLDB	7.0558203e-05
4,125	Computing Clusters of Correlation Connected Objects	2004	SIGMOD	6.4248096e-05

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