HD-Eye: Visual Clustering of High Dimensional Data

Summary: HD-Eye integrates high-dimensional clustering with visualization to guide the clustering process and interpret results. Tight coupling of advanced clustering algorithms and visualization empowers multiple paradigms and improves clustering outcomes. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 3466
Venue: SIGMOD
Year: 2002
Pagerank: -
Overall Rank: 13,965 | 4.19%
DOI: 10.1145/564691.564784

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Authors

1. Alexander Hinneburg (University of Halle)
2. Daniel A. Keim (AT&T; University of Konstanz)
3. Markus Wawryniuk (University of Konstanz)

BibTeX Citation

@inproceedings{hinneburg_sigmod02,
        title = {{HD-Eye: Visual Clustering of High Dimensional Data}},
        author = {Hinneburg, Alexander and Keim, Daniel A. and Wawryniuk, Markus},
        series = {{SIGMOD} '02},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/564691.564784},
        url = {https://dl.acm.org/doi/10.1145/564691.564784},
        year = {2002}
}

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