Structural Decomposition Trees

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dc.contributor.authorEngel, Danielen_US
dc.contributor.authorRosenbaum, R.en_US
dc.contributor.authorHamann, B.en_US
dc.contributor.authorHagen, Hansen_US
dc.contributor.editorH. Hauser, H. Pfister, and J. J. van Wijken_US
dc.date.accessioned2014-02-21T20:23:37Z
dc.date.available2014-02-21T20:23:37Z
dc.date.issued2011en_US
dc.description.abstractResearchers and analysts in modern industrial and academic environments are faced with a daunting amount of multi-dimensional data. While there has been significant development in the areas of data mining and knowledge discovery, there is still the need for improved visualizations and generic solutions. The state-of-the-art in visual analytics and exploratory data visualization is to incorporate more profound analysis methods while focusing on fast interactive abilities. The common trend in these scenarios is to either visualize an abstraction of the data set or to better utilize screen-space. This paper presents a novel technique that combines clustering, dimension reduction and multi-dimensional data representation to form a multivariate data visualization that incorporates both detail and overview. This amalgamation counters the individual drawbacks of common projection and multi-dimensional data visualization techniques, namely ambiguity and clutter. A specific clustering criterion is used to decompose a multi-dimensional data set into a hierarchical tree structure. This decomposition is embedded in a novel Dimensional Anchor visualization through the use of a weighted linear dimension reduction technique. The resulting Structural Decomposition Tree (SDT) provides not only an insight of the data set's inherent structure, but also conveys detailed coordinate value information. Further, fast and intuitive interaction techniques are explored in order to guide the user in highlighting, brushing, and filtering of the data.en_US
dc.description.number3en_US
dc.description.seriesinformationComputer Graphics Forumen_US
dc.description.volume30en_US
dc.identifier.doi10.1111/j.1467-8659.2011.01941.xen_US
dc.identifier.issn1467-8659en_US
dc.identifier.urihttps://doi.org/10.1111/j.1467-8659.2011.01941.xen_US
dc.publisherThe Eurographics Association and Blackwell Publishing Ltd.en_US
dc.subjectI.4.4 [IMAGE PROCESSING AND COMPUTER VISION]en_US
dc.subjectImage Representationen_US
dc.subjectMultidimensionalen_US
dc.subjectI.5.4 [PATTERN RECOGNITION]en_US
dc.subjectClusteringen_US
dc.subjectSimilarity measuresen_US
dc.titleStructural Decomposition Treesen_US
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EuroVis11: Eurographics/ IEEE Symposium on Visualization