Computing Clusters of Correlation Connected Objects

Summary: 4C (Computing Correlation Connected Clusters) finds local subgroups with uniform, arbitrarily complex correlations beyond global linear patterns. It blends PCA and DBSCAN to yield determinate, noise-robust clusters and beats DBSCAN, CLIQUE, ORCLUS in tests. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 3602
Venue: SIGMOD
Year: 2004
Pagerank: 6.4752952e-05
Overall Rank: 4,856 | 66.69%
DOI: 10.1145/1007568.1007620

Incoming Non-self Citations Over Time

Authors

1. Christian Böhm (University of Munich)
2. Karin Kailing (University of Munich)
3. Peer Kröger (University of Munich)
4. Arthur Zimek (University of Munich)

BibTeX Citation

@inproceedings{bohm_sigmod04,
        title = {{Computing Clusters of Correlation Connected Objects}},
        author = {Böhm, Christian and Kailing, Karin and Kröger, Peer and Zimek, Arthur},
        series = {{SIGMOD} '04},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/1007568.1007620},
        url = {https://dl.acm.org/doi/10.1145/1007568.1007620},
        year = {2004}
}

Incoming Citations (Sorted by Pagerank)

Showing 5 of 5 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
962	DBSCAN Revisited: Mis-Claim, Un-Fixability, and Approximation	2015	SIGMOD	0.00012936472
6,205	Outlier-robust Clustering using Independent Components	2008	SIGMOD	5.9443504e-05
8,215	CURLER: Finding and Visualizing Nonlinear Correlation Clusters	2005	SIGMOD	5.4651579e-05
12,238	Interactive Data Mining with 3D-Parallel-Coordinate-Trees	2013	SIGMOD	5.093636e-05
12,602	Detecting Clusters in Moderate-to-High Dimensional Data: Subspace Clustering, Pattern-based Clustering, and Correlation Clustering	2008	VLDB	5.093636e-05

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.

Rank	Cited Paper	Year	Venue	Pagerank
85	The X-tree: An Index Structure for High-Dimensional Data	1996	VLDB	0.00035405879
291	OPTICS: Ordering Points To Identify the Clustering Structure	1999	SIGMOD	0.00022264197
304	Automatic Subspace Clustering of High Dimensional Data for Data Mining Applications	1998	SIGMOD	0.00021917388
1,602	Local Dimensionality Reduction: A New Approach to Indexing High Dimensional Spaces	2000	VLDB	0.00010239526
1,646	Fast Algorithms for Projected Clustering	1999	SIGMOD	0.00010128564
1,833	Finding Generalized Projected Clusters in High Dimensional Spaces	2000	SIGMOD	9.6566846e-05
3,550	A Monte Carlo Algorithm for Fast Projective Clustering	2002	SIGMOD	7.3200789e-05
4,812	Clustering by Pattern Similarity in Large Data Sets	2002	SIGMOD	6.4980201e-05

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#	Overall Rank	Paper	Year	Venue
1	1,646	Fast Algorithms for Projected Clustering	1999	SIGMOD
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5	6,205	Outlier-robust Clustering using Independent Components	2008	SIGMOD
6	8,215	CURLER: Finding and Visualizing Nonlinear Correlation Clusters	2005	SIGMOD
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8	12,572	Constrained Locally Weighted Clustering	2008	VLDB
9	12,602	Detecting Clusters in Moderate-to-High Dimensional Data: Subspace Clustering, Pattern-based Clustering, and Correlation Clustering	2008	VLDB
10	8,925	Multivariate Correlations Discovery in Static and Streaming Data	2022	VLDB