Cabinet Tree : an orthogonal enclosure approach to visualizing and exploring big data

Yalong Yang, Kang Zhang, Jianrong Wang, Quang Vinh Nguyen

    Research output: Contribution to journalArticlepeer-review

    4 Citations (Scopus)

    Abstract

    Treemaps are well-known for visualizing hierarchical data. Most related approaches have been focused on layout algorithms and paid little attention to other display properties and interactions. Furthermore, the structural information in conventional Treemaps is too implicit for viewers to perceive. This paper presents Cabinet Tree, an approach that: i) draws branches explicitly to show relational structures, ii) adapts a space-optimized layout for leaves and maximizes the space utilization, iii) uses coloring and labeling strategies to clearly reveal patterns and contrast different attributes intuitively. We also apply the continuous node selection and detail window techniques to support user interaction with different levels of the hierarchies. Our quantitative evaluations demonstrate that Cabinet Tree achieves good scalability for increased resolutions and big datasets.
    Original languageEnglish
    Number of pages18
    JournalJournal of Big Data
    Volume2
    Issue number15
    DOIs
    Publication statusPublished - 2015

    Open Access - Access Right Statement

    © Yang et al. 2015 This is an Open Access article distributed under the terms of the Creative Commons Attribution License(http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

    Keywords

    • Treemaps
    • hierarchical clustering (cluster analysis)
    • orthogonal enclosure

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