Design and Analysis of Subspace Clustering Algorithms and their Applicability

Summary: Studies subspace clustering for high-dimensional data, emphasizing efficient dense-unit discovery via sampling, fewer database scans, and reduced candidate counting. Extends applicability to census, support-constrained, mixed-type, rare-attribute, and streaming data. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID: 9135
Venue: VLDB
Year: 2002
Pagerank: -
Overall Rank: 14,001 | 3.95%
DOI: -

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Authors

1. Jyoti D. Pawar (Goa University)

BibTeX Citation

@article{pawar_vldb02,
        title = {{Design and Analysis of Subspace Clustering Algorithms and their Applicability}},
        author = {Pawar, Jyoti D.},
        journal = {PVLDB},
        series = {{VLDB} '02},
        year = {2002}
}

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