ClustNails: Visual analysis of subspace clusters

Andrada Tatu, Leishi Zhang, Enrico Bertini, Tobias Schreck, Daniel Keim, Sebastian Bremm, Tatiana Von Landesberger

    Research output: Contribution to journalArticlepeer-review


    Subspace clustering addresses an important problem in clustering multi-dimensional data. In sparse multi-dimensional data, many dimensions are irrelevant and obscure the cluster boundaries. Subspace clustering helps by mining the clusters present in only locally relevant subsets of dimensions. However, understanding the result of subspace clustering by analysts is not trivial. In addition to the grouping information, relevant sets of dimensions and overlaps between groups, both in terms of dimensions and records, need to be analyzed. We introduce a visual subspace cluster analysis system called ClustNails. It integrates several novel visualization techniques with various user interaction facilities to support navigating and interpreting the result of subspace clustering. We demonstrate the effectiveness of the proposed system by applying it to the analysis of real world data and comparing it with existing visual subspace cluster analysis systems.

    Original languageEnglish (US)
    Article number6297588
    Pages (from-to)419-428
    Number of pages10
    JournalTsinghua Science and Technology
    Issue number4
    StatePublished - 2012


    • Data exploration
    • Pixel-based techniques
    • Subspace cluster analysis
    • Visualization

    ASJC Scopus subject areas

    • General


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