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Choosing Visualization Techniques for Multidimensional Data Projection Tasks: A Guideline with Examples

  • Conference paper
Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2015)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 598))

Abstract

This paper presents a guideline for visualization designers who want to choose appropriate techniques for enhancing tasks involving multidimensional projection. Specifically, we adopt a user-centric approach in which we take user perception into consideration. Here, we focus on projection techniques that output 2D or 3D scatterplots that can then be used for a range of common data analysis tasks, which we categorize as pattern identification tasks, relation-seeking tasks, membership disambiguation tasks, or behavior comparison tasks. Our user-centric task categorization can be used to effectively guide the organization of multidimensional data projection layouts. Moreover, we present real-world examples that demonstrate effective choices made by visualization designers faced with complex datasets requiring dimensionality reduction.

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Notes

  1. 1.

    CBR comprises 680 documents, which include title, authors, abstract, and references from scientific papers in the four different subjects, leading to a data set with 680 objects and 1,423 dimensions. KDViz data has been generated from an Internet repository on the topics bibliographic coupling, co-citation analysis, milgrams, and information visualization, leading to 1,624 objects, 520 dimensions, and four highly unbalanced labels (http://vicg.icmc.usp.br/infovis2/DataSets).

  2. 2.

    1,000 photographs on ten different themes. Each image is represented by a 150-dimensional vector of SIFT descriptors (3UCI KDD Archive, http://kdd.ics.uci.edu).

  3. 3.

    Each image is represented by 28 features, including Fourier descriptors and energies derived from histograms, as well as mean intensity and standard deviation computed from the images themselves. Hence, the data set contains 540 objects and 28 dimensions.

  4. 4.

    ETHZ represents a subset of the ETHZ dataset [13, 38], with 2019 photographs of different people captured in uncontrolled conditions. It is divided into 28 unbalanced groups, and each image is represented by a vector of 3963 descriptors, combining Gabor filters, Histogram of Oriented Gradients (HOG), Local Binary Patterns (LBP) and mean intensity.

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Author information

Authors and Affiliations

  1. Oklahoma State University, Stillwater, OK, USA

    Ronak Etemadpour & Christopher Crick

  2. Jacobs University, Bremen, Germany

    Lars Linsen

  3. Federal University of Uberlandia, Uberlandia, MG, Brazil

    Jose Gustavo Paiva

  4. University of Illinois at Chicago, Chicago, IL, USA

    Angus Graeme Forbes

Authors
  1. Ronak Etemadpour
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  2. Lars Linsen
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  3. Jose Gustavo Paiva
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  4. Christopher Crick
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  5. Angus Graeme Forbes
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Correspondence to Ronak Etemadpour .

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Editors and Affiliations

  1. Escola Superior de Tecnologia do IPS, Setúbal, Portugal

    José Braz

  2. Inria-Rennes/MimeTIC Team, Rennes cedex, France

    Julien Pettré

  3. LISA - ISTIA, University of Angers, Angers, France

    Paul Richard

  4. Linnaeus University, Växjö, Sweden

    Andreas Kerren

  5. Jacobs University, Bremen, Germany

    Lars Linsen

  6. Università di Catania, Catania, Catania, Italy

    Sebastiano Battiato

  7. Research Innovation Center, Canon U.S.A. Inc, San Jose, CA, USA

    Francisco Imai

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Etemadpour, R., Linsen, L., Paiva, J.G., Crick, C., Forbes, A.G. (2016). Choosing Visualization Techniques for Multidimensional Data Projection Tasks: A Guideline with Examples. In: Braz, J., et al. Computer Vision, Imaging and Computer Graphics Theory and Applications. VISIGRAPP 2015. Communications in Computer and Information Science, vol 598. Springer, Cham. https://doi.org/10.1007/978-3-319-29971-6_9

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  • DOI: https://doi.org/10.1007/978-3-319-29971-6_9

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