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The Visual SuperTree: similarity-based multi-scale visualization

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Abstract

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.

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References

  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)

  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)

  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)

  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)

    Article  Google Scholar 

  5. Bifet, A., Holmes, G., Kirkby, R., Pfahringer, B.: MOA: massive online analysis. J. Mach. Learn. Res. 11, 1601–1604 (2010)

    Google Scholar 

  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)

  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)

    Article  Google Scholar 

  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)

  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)

  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)

    Article  Google Scholar 

  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)

  12. Gascuel, O., Steel, M.: Neighbor-joining revealed. Mol. Biol. Evol. 23(11), 1997–2000 (2006)

    Article  Google Scholar 

  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)

  14. Ingram, S., Munzner, T., Olano, M.: Glimmer: multilevel MDS on the GPU. IEEE Trans. Vis. Comput. Graph. 15(2), 249–261 (2009)

    Article  Google Scholar 

  15. Jain, A.K., Murty, M.N., Flynn, P.J.: Data clustering: a review. ACM Comput. Surv. 31(3), 264–323 (1999)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  18. van der Maaten, L.: Accelerating t-SNE using tree-based algorithms. J. Mach. Learn. Res. 15(1), 3221–3245 (2014)

    MathSciNet  MATH  Google Scholar 

  19. van der Maaten, L., Hinton, G.E.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008)

    MATH  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  22. Nocaj, A., Brandes, U.: Computing Voronoi treemaps: faster, simpler, and resolution-independent. Comput. Graph. Forum 31(3pt1), 855–864 (2012)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. Schulz, H.J.: Treevis.net: a tree visualization reference. IEEE Comput. Graph. Appl. 31(6), 11–15 (2011)

    Article  Google Scholar 

  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)

  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)

  33. Tan, L., Song, Y., Liu, S., Xie, L.: ImageHive: interactive content-aware image summarization. IEEE Comput. Graph. Appl. 32(1), 46–55 (2012)

    Article  Google Scholar 

  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. 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)

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Acknowledgements

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

Funding

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.

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Authors and Affiliations

  1. University of São Paulo, São Carlos, SP, Brazil

    Renato R. O. da Silva, Carlos E. A. Zampieri, Fábio P. Rolli & Rosane Minghim

  2. Federal University of Uberlândia, Uberlândia, MG, Brazil

    José Gustavo S. Paiva

  3. University of Campinas, Campinas, SP, Brazil

    Guilherme P. Telles

  4. Federal University of Grande Dourados, Dourados, MS, Brazil

    Carlos E. A. Zampieri

  5. University of Groningen, Groningen, The Netherlands

    Renato R. O. da Silva

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  1. Renato R. O. da Silva
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  2. José Gustavo S. Paiva
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  3. Guilherme P. Telles
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  4. Carlos E. A. Zampieri
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  5. Fábio P. Rolli
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  6. Rosane Minghim
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Correspondence to Renato R. O. da Silva.

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da Silva, R.R.O., Paiva, J.G.S., Telles, G.P. et al. The Visual SuperTree: similarity-based multi-scale visualization. Vis Comput 35, 1067–1080 (2019). https://doi.org/10.1007/s00371-019-01696-5

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