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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
he963907cdfcba70a
Venue
SIGMOD
Year
2011
Pagerank
5.2559789e-05
Overall Rank
8,914 | 40.07%
DOI
10.1145/1989323.1989400

Incoming Non-self Citations Over Time

Authors

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
11,839 The Gibbs–Rand Model 2022 PODS 4.9793485e-05
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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
1,679 Fast Algorithms for Projected Clustering 1999 SIGMOD 9.9079414e-05
3,618 A Monte Carlo Algorithm for Fast Projective Clustering 2002 SIGMOD 7.1559402e-05
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