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Evaluating Clustering in Subspace Projections of High Dimensional Data

Summary: Systematic benchmark of subspace clustering in high-dimensional data. Evaluates paradigms under a common framework; addresses ground-truth absence, varied metrics, and cross-paradigm comparison; provides real/synthetic datasets and repeatable baselines. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
10108
Venue
VLDB
Year
2009
Pagerank
5.3753402e-05
Overall Rank
8,778 | 39.78%
DOI
10.14778/1687627.1687770

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{muller_vldb09,
        title = {{Evaluating Clustering in Subspace Projections of High Dimensional Data}},
        author = {Müller, Emmanuel and Günnemann, Stephan and Assent, Ira and Seidl, Thomas},
        journal = {PVLDB},
        series = {{VLDB} '09},
        doi = {10.14778/1687627.1687770},
        url = {https://doi.org/10.14778/1687627.1687770},
        year = {2009}
}

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