Skip to main content
SpringerLink
Log in

Scatterplot layout for high-dimensional data visualization

  • Regular Paper
  • Published:
Journal of Visualization Aims and scope Submit manuscript

Abstract

Multi-dimensional data visualization is an important research topic that has been receiving increasing attention. Several techniques that apply scatterplot matrices have been proposed to represent multi-dimensional data as a collection of two-dimensional data visualization spaces. Typically, when using the scatterplot-based approach it is easier to understand relations between particular pairs of dimensions, but it often requires too large display spaces to display all possible scatterplots. This paper presents a technique to display meaningful sets of scatterplots generated from high-dimensional datasets. Our technique first evaluates all possible scatterplots generated from high-dimensional datasets, and selects meaningful sets. It then calculates the similarity between arbitrary pairs of the selected scatterplots, and places relevant scatterplots closer together in the display space while they never overlap each other. This design policy makes users easier to visually compare relevant sets of scatterplots. This paper presents algorithms to place the scatterplots by the combination of ideal position calculation and rectangle packing algorithms, and two examples demonstrating the effectiveness of the presented technique.

Graphical Abstract

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  • Correa CD, Crnovrsanin T, Ma K-L (2012) Visual reasoning about social networks using centrality sensitivity. IEEE Trans Vis Comput Gr 18(1):106–120

    Article  Google Scholar 

  • Elmqvist N, Dragicevic P, Fekete J (2008) Rolling the dice: multidimensional visual exploration using scatterplot matrix navigation. IEEE Trans Vis Comput Gr 14(6):1141–1148

    Article  Google Scholar 

  • Feiner S, Beschers C (1990) Worlds within worlds: metaphors for exploring n-dimensional virtual worlds, ACM symposium on user interface software and technology (UIST ’90), pp 76–83

  • Grinstein G, Trutschl M, Cvek U (2001) High-dimensional visualizations, KDD workshop on visual data mining

  • Harel D, Koren Y (2002) Drawing graphs with nonuniform vertices. Adv Vis Interfaces AVI 157–166:2002

    Google Scholar 

  • Inselberg A, Dimsdale B (1990) Parallel coordinate: a tool for visualizing multi-dimensional geometry. In: IEEE Visualization, pp 361–370

  • Itoh T, Takakura H, Sawada A, Koyamada K (2006) Hierarchical visualization of network intrusion detection data in the IP address space. IEEE Comput Graphics Appl 26(2):40–47

    Article  Google Scholar 

  • Itoh T, Muelder C, Ma K, Sese J (2009) A hybrid space-filling and force-directed layout method for visualizing multiple-category graphs. IEEE Pac Vis Symp, pp 121–128

  • Keim DA, Kriegel H-P (1994) VisDB: database eploration usng multidimensional visualization. IEEE Comput Graphics Appl 14(5):40–49

    Article  Google Scholar 

  • Leban G, Bratko I, Petrovics U, Curk T, Zupan B (2005) VizRank: finding informative data projections in functional genomics by machine learning. Bioinform Appl Note 21(3):413–414

    Article  Google Scholar 

  • Lin C, Yen H, Chuang J (2009) Drawing graphs with nonuniform nodes using potential fields. J Vis Lang Comput 20(6):385–402

    Article  Google Scholar 

  • Mihalisin T, Gawlinski E, Timlin J, Schwegler J (1990) Visualizing a scalar field on an n-dimensional lattice. In: IEEE Visualization, pp 255–262

  • Sips M (2009) Selecting good views of high-dimensional data using class consistency? In: Eurographics/IEEE VGTC Symposium on Visualization, pp 831–838

  • UCI Machine Learning Repository, Image segmentation data set, http://archive.ics.uci.e.du/ml/datasets/Image+Segmentation

  • Ward MO (1994) XmdvTool: integrating multiple methods for visualizing multivariate data. In: IEEE Visualization, pp 326–336

  • Wilkinson L, Anand A, Grossman R (2005) Graph-theoretic scagnostics. In: IEEE Symposium on information visualization, pp 157–164

  • Wong PC, Bergeron RD (1997) 30 years of multidimensional multivariate visualization, scientific visualization: overviews methodologies and techniques. IEEE Comput Soc Press, pp 3–33

  • YAHOO! JAPAN Automobile, http://autos.yahoo.co.jp/

Download references

Author information

Authors and Affiliations

  1. Department of Information Sciences, Ochanomizu University, 2-1-1 Otsuka, Bunkyo-ku, Tokyo , 112-8610, Japan

    Yunzhu Zheng, Haruka Suematsu & Takayuki Itoh

  2. NEC Laboratories America, Cupertino, USA

    Ryohei Fujimaki

  3. Osaka University, Osaka, Japan

    Satoshi Morinaga

  4. NEC, Kawasaki, Japan

    Yoshinobu Kawahara

Authors
  1. Yunzhu Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Haruka Suematsu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Takayuki Itoh
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ryohei Fujimaki
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Satoshi Morinaga
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Yoshinobu Kawahara
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Takayuki Itoh.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zheng, Y., Suematsu, H., Itoh, T. et al. Scatterplot layout for high-dimensional data visualization. J Vis 18, 111–119 (2015). https://doi.org/10.1007/s12650-014-0230-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12650-014-0230-5

Keywords

Search

Navigation