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3D Grand Tour for Multidimensional Data and Clusters

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Advances in Intelligent Data Analysis (IDA 1999)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1642))

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Abstract

Grand tour is a method for viewing multidimensional data via linear projections onto a sequence of two dimensional subspaces and then moving continuously from one projection to the next. This paper extends the method to 3D grand tour where projections are made onto three dimensional subspaces. 3D cluster-guided tour is proposed where sequences of projections are determined by cluster centroids. Cluster-guided tour makes inter-cluster distance-preserving projections under which clusters are displayed as separate as possible. Various add-on features, such as projecting variable vectors together with data points, inter-active picking and drill down, and cluster similarity graphs, help further the understanding of data. A CAVE virtual reality environment is at our disposal for 3D immersive display. This approach of multidimensional visualization provides a natural metaphor to visualize clustering results and data at hand by mapping the data onto a time-indexed family of 3D natural projections suitable for human eye’s exploration.

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Author information

Authors and Affiliations

  1. National University of Singapore, Institute of High Performance Computing, 89 Science Park Drive, #01-05/08, The Rutherford, Singapore, 118261

    Li Yang

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  1. Li Yang
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Editor information

Editors and Affiliations

  1. Department of Mathematics, Imperial College, Huxley Building 180 Queen’s Gate, London, SW7 2BZ, UK

    David J. Hand

  2. Leiden Institute for Advanced Computer Science, Leiden University, 2300, RA Leiden, The Netherlands

    Joost N. Kok

  3. Berkeley Initiative in Soft Computing, University of California at Berkeley, 329 Soda Hall, Berkeley, CA, 94720, USA

    Michael R. Berthold

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© 1999 Springer-Verlag Berlin Heidelberg

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Yang, L. (1999). 3D Grand Tour for Multidimensional Data and Clusters. In: Hand, D.J., Kok, J.N., Berthold, M.R. (eds) Advances in Intelligent Data Analysis. IDA 1999. Lecture Notes in Computer Science, vol 1642. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48412-4_15

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  • DOI: https://doi.org/10.1007/3-540-48412-4_15

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-66332-4

  • Online ISBN: 978-3-540-48412-7

  • eBook Packages: Springer Book Archive

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