Advancing Data Clustering via Projective Clustering Ensembles

Summary: Single-objective PCE unifies object- and feature-based representations via distance to the ensemble, addressing independence issues of two-objective PCE. Two cluster-based approximations align with standard ensemble paradigms; benchmarks show improved results over prior PCE methods. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 4489
Venue: SIGMOD
Year: 2011
Pagerank: 5.3766157e-05
Overall Rank: 8,751 | 39.97%
DOI: 10.1145/1989323.1989400

Incoming Non-self Citations Over Time

Authors

1. Francesco Gullo (University of Calabria)
2. Carlotta Domeniconi (George Mason University)
3. Andrea Tagarelli (University of Calabria)

BibTeX Citation

@inproceedings{gullo_sigmod11,
        title = {{Advancing Data Clustering via Projective Clustering Ensembles}},
        author = {Gullo, Francesco and Domeniconi, Carlotta and Tagarelli, Andrea},
        series = {{SIGMOD} '11},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/1989323.1989400},
        url = {https://dl.acm.org/doi/10.1145/1989323.1989400},
        year = {2011}
}

Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
11,530	The Gibbs–Rand Model	2022	PODS	5.093636e-05

Outgoing Citations (Sorted by Pagerank)

Showing 2 of 2 cited papers.

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

Rank	Cited Paper	Year	Venue	Pagerank
1,646	Fast Algorithms for Projected Clustering	1999	SIGMOD	0.00010128564
3,550	A Monte Carlo Algorithm for Fast Projective Clustering	2002	SIGMOD	7.3200789e-05

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