The Visual Computer

, Volume 35, Issue 6–8, pp 1067–1080 | Cite as

The Visual SuperTree: similarity-based multi-scale visualization

  • Renato R. O. da SilvaEmail author
  • José Gustavo S. Paiva
  • Guilherme P. Telles
  • Carlos E. A. Zampieri
  • Fábio P. Rolli
  • Rosane Minghim
Original Article


Similarity-based exploration of multi-dimensional data sets is a difficult task, in which most techniques do not perform well with large data sets, particularly in handling clutter that invariably happens as data sets grow larger. In this paper, we introduce the Visual SuperTree (VST), a method to build a multi-scale similarity tree that can deal with large data sets at interactive rates, maintaining most of the accuracy and the data organization capabilities of other available methods. The VST is built on top of a clustered multi-level configuration of the data that allows the user to quickly explore data sets by similarity. The method is shown to be useful for both unlabeled and labeled data, and it is capable of revealing external and internal cluster structures. We demonstrate its application on artificial and real data sets, showing additional advantages of the approach when exploring data that can be summarized meaningfully.


Similarity trees Multi-dimensional data Multi-scale visualization Image and text visualization 



We would like to thank the reviewers for their helpful suggestions.


This work was funded by the São Paulo Research Foundation (FAPESP), Grant 2011/18838-5, and the National Council for Scientific and Technological Development (CNPq), Grants 307411/2016-8 and 310299/2018-7.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Supplementary material

Supplementary material 1 (mp4 9879 KB)


  1. 1.
    Bachmaier, C., Brandes, U., Schlieper, B.: Drawing phylogenetic trees. In: Proceedings of International Symposium of Algorithms and Computation, vol. 3827, pp. 1110–1121 (2005)Google Scholar
  2. 2.
    Balzer, M., Deussen, O., Lewerentz, C.: Voronoi treemaps for the visualization of software metrics. In: Proceedings of ACM Symposium on Software Visualization, pp. 165–172. New York, NY, USA (2005)Google Scholar
  3. 3.
    Bederson, B.B.: PhotoMesa: a zoomable image browser using quantum treemaps and bubblemaps. In: Proceedings of Annual ACM Symposium on User Interface Software and Technology, pp. 71–80. ACM, Orlando, FL, USA (2001)Google Scholar
  4. 4.
    Bederson, B.B., Shneiderman, B., Wattenberg, M.: Ordered and quantum treemaps: making effective use of 2D space to display hierarchies. ACM Trans. Graph. 21(4), 833–854 (2002)CrossRefGoogle Scholar
  5. 5.
    Bifet, A., Holmes, G., Kirkby, R., Pfahringer, B.: MOA: massive online analysis. J. Mach. Learn. Res. 11, 1601–1604 (2010)Google Scholar
  6. 6.
    Chalmers, M.: A linear iteration time layout algorithm for visualising high-dimensional data. In: Proceedings of Visualization 1996, pp. 127–131. San Francisco, CA, USA (1996)Google Scholar
  7. 7.
    Cockburn, A., Karlson, A.K., Bederson, B.B.: A review of overview + detail, zooming, and focus + context interfaces. ACM Comput. Surv. 41(1), 1–31 (2008)CrossRefGoogle Scholar
  8. 8.
    Cuadros, A.M., Paulovich, F.V., Minghim, R., Telles, G.P.: Point placement by phylogenetic trees and its application for visual analysis of document collections. In: IEEE Symposium on Visual Analytics Science and Technology, pp. 99–106. Sacramento, CA, USA (2007)Google Scholar
  9. 9.
    Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255. Miami, FL, USA (2009)Google Scholar
  10. 10.
    Eler, D.M., Nakazaki, M.Y., Paulovich, F.V., Santos, D.P., Andery, G.F., Oliveira, M.C.F., Batista-Neto, J., Minghim, R.: Visual analysis of image collections. Vis. Comput. 25(10), 923–937 (2009)CrossRefGoogle Scholar
  11. 11.
    Fei-Fei, L., Fergus, R., Perona, P.: Learning generative visual models from few training examples: an incremental bayesian approach tested on 101 object categories. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 178–178. Los Alamitos, CA, USA (2004)Google Scholar
  12. 12.
    Gascuel, O., Steel, M.: Neighbor-joining revealed. Mol. Biol. Evol. 23(11), 1997–2000 (2006)CrossRefGoogle Scholar
  13. 13.
    Gomi, A., Miyazaki, R., Itoh, T., Li, J.: CAT: A hierarchical image browser using a rectangle packing technique. In: Proceedings of International Conference Information Visualisation, pp. 82–87. Columbus, OH, USA (2008)Google Scholar
  14. 14.
    Ingram, S., Munzner, T., Olano, M.: Glimmer: multilevel MDS on the GPU. IEEE Trans. Vis. Comput. Graph. 15(2), 249–261 (2009)CrossRefGoogle Scholar
  15. 15.
    Jain, A.K., Murty, M.N., Flynn, P.J.: Data clustering: a review. ACM Comput. Surv. 31(3), 264–323 (1999)CrossRefGoogle Scholar
  16. 16.
    Joia, P., Coimbra, D., Cuminato, J.A., Paulovich, F.V., Nonato, L.G.: Local affine multidimensional projection. IEEE Trans. Vis. Comput. Graph. 17, 2563–2571 (2011)CrossRefGoogle Scholar
  17. 17.
    Li, J., Wang, J.Z.: Automatic linguistic indexing of pictures by a statistical modeling approach. IEEE Trans. Pattern Anal. Mach. Intell. 25, 1075–1088 (2003)CrossRefGoogle Scholar
  18. 18.
    van der Maaten, L.: Accelerating t-SNE using tree-based algorithms. J. Mach. Learn. Res. 15(1), 3221–3245 (2014)MathSciNetzbMATHGoogle Scholar
  19. 19.
    van der Maaten, L., Hinton, G.E.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008)zbMATHGoogle Scholar
  20. 20.
    Neves, T.T., Fadel, S.G., Hilasaca, G.M., Fatore, F.M., Paulovich, F.V.: Updis: a user-assisted projection technique for distance information. Inf. Vis. 17(4), 269–281 (2018)CrossRefGoogle Scholar
  21. 21.
    Nguyen, Q.V., Huang, M.L.: Space-optimized tree: a connection + enclosure approach for the visualization of large hierarchies. Inf. Vis. 2(1), 3–15 (2003)CrossRefGoogle Scholar
  22. 22.
    Nocaj, A., Brandes, U.: Computing Voronoi treemaps: faster, simpler, and resolution-independent. Comput. Graph. Forum 31(3pt1), 855–864 (2012)CrossRefGoogle Scholar
  23. 23.
    Paiva, J.G., Florian, L., Pedrini, H., Telles, G., Minghim, R.: Improved similarity trees and their application to visual data classification. IEEE Trans. Vis. Comput. Graph. 17(12), 2459–2468 (2011)CrossRefGoogle Scholar
  24. 24.
    Pal, N.R., Bezdek, J.C.: On cluster validity for the fuzzy c-means model. IEEE Trans. Fuzzy Syst. 3(3), 370–379 (1995)CrossRefGoogle Scholar
  25. 25.
    Pass, G., Zabih, R., Miller, J.: Comparing images using color coherence vectors. In: Proceedings of ACM International Conference on Multimedia, pp. 65–73. Boston, Massachusetts, USA (1996)Google Scholar
  26. 26.
    Paulovich, F.V., Nonato, L.G., Minghim, R., Levkowitz, H.: Least square projection: a fast high precision multidimensional projection technique and its application to document mapping. IEEE Trans. Vis. Comput. Graph. 14(3), 564–575 (2008)CrossRefGoogle Scholar
  27. 27.
    Paulovich, F.V., Telles, G.P., Toledo, F.M.B., Minghim, R., Nonato, L.G.: Semantic wordification of document collections. Comput. Graph. Forum 31(3pt3), 1145–1153 (2012)CrossRefGoogle Scholar
  28. 28.
    Pavlopoulos, G.A., Soldatos, T.G., Barbosa-Silva, A., Schneider, R.: A reference guide for tree analysis and visualization. BioData Min. 3(1), 1–24 (2010)CrossRefGoogle Scholar
  29. 29.
    Saitou, N., Nei, M.: The neighbor-joining method: a new method for reconstructing phylogenetic trees. Mol. Biol. Evol. 4(4), 406–425 (1987)Google Scholar
  30. 30.
    Schulz, H.J.: a tree visualization reference. IEEE Comput. Graph. Appl. 31(6), 11–15 (2011)CrossRefGoogle Scholar
  31. 31.
    Schulz, H.J., Hadlak, S., Schumann, H.: Point-based tree representation: a new approach for large hierarchies. In: Proceeding of IEEE Pacific Visualization Symposium, pp. 81–88. Beijing, China (2009)Google Scholar
  32. 32.
    Stehling, R.O., Nascimento, M.A., Falcão, A.X.: A compact and efficient image retrieval approach based on border/interior pixel classification. In: Proceedings of International Conference on Information and Knowledge Management, pp. 102–109. McLean, Virginia, USA (2002)Google Scholar
  33. 33.
    Tan, L., Song, Y., Liu, S., Xie, L.: ImageHive: interactive content-aware image summarization. IEEE Comput. Graph. Appl. 32(1), 46–55 (2012)CrossRefGoogle Scholar
  34. 34.
    Telles, G.P., Araújo, G.S., Walter, M.E.M.T., Brigido, M.M., Almeida, N.F.: Live neighbor-joining. BMC Bioinf. 19(172), 1–13 (2018)Google Scholar
  35. 35.
    Ying, A.T.T.: Mining Challenge 2015: Comparing and combining different information sources on the Stack Overflow data set. In: Proceedings of Working Conference on Mining Software Repositories (2015)Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.University of São PauloSão CarlosBrazil
  2. 2.Federal University of UberlândiaUberlândiaBrazil
  3. 3.University of CampinasCampinasBrazil
  4. 4.Federal University of Grande DouradosDouradosBrazil
  5. 5.University of GroningenGroningenThe Netherlands

Personalised recommendations