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Visual Exploration of Multivariate Volume Data Based on Clustering

  • Lars Linsen
Chapter
Part of the Mathematics and Visualization book series (MATHVISUAL)

Abstract

The attribute space of a multivariate volume data set is hard to handle interactively in the context of volume visualization when more than three attributes are involved. Automatic or semi-automatic approaches such as involving clustering help to reduce the complexity of the problem. Clustering methods segment the attribute space, and the segmentation can be exploited for visual exploration of the volume data. We discuss user-guided and automatic clustering approaches of the multi-dimensional attribute space and visual representations of the results. Coordinated views of object-space volume visualization with attribute-space clustering results can be applied for interactive visual exploration of the multivariate volume data and even for interactive modification of the clustering results. Respective methods are presented and discussed and future directions are outlined.

Keywords

Independent Component Analysis Attribute Space Cluster Result Object Space Independent Component Analysis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Akiba, H., Ma, K.L.: A tri-space visualization interface for analyzing time-varying multivariate volume data. In: Proceedings of Eurographics/IEEE VGTC Symposium on Visualization, pp. 115–122, May 2007Google Scholar
  2. 2.
    Akiba, H., Ma, K.L: A tri-space visualization interface for analyzing time-varying multivariate volume data. In: EuroVis07—Eurographics/IEEE VGTC Symposium on Visualization, pp. 115–122, May 2007Google Scholar
  3. 3.
    Akiba, H., Ma, K.L., Chen, J.H., Hawkes, E.R.: Visualizing multivariate volume data from turbulent combustion simulations. Comput. Sci. Engg. 9(2), 76–83 (2007)CrossRefGoogle Scholar
  4. 4.
    Andreas K.ö.: A survey of methods for multivariate data projection, visualisation and interactive analysis. In: Yamakawa, T. (ed.) 5th International Conference on Soft Computing and Information/Intelligent Systems, pp. 55–59, Iizuka, Japan, Oct 16–20 (1998)Google Scholar
  5. 5.
    Bachthaler, S., Weiskopf, D.: Continuous scatterplots. IEEE Trans. Vis. Comput. Graph. (Proceedings Visualization/Information Visualization 2008), 14(6), 1428–1435, Nov–Dec 2008Google Scholar
  6. 6.
    Bachthaler, S., Weiskopf, D.: Efficient and adaptive rendering of 2-d continuous scatterplots. vol. 28, pp. 743–750 (2009)Google Scholar
  7. 7.
    Bezdek, J.C.: Pattern recognition with fuzzy objective function algorithms. Plenum Press, New York (1981)CrossRefMATHGoogle Scholar
  8. 8.
    Blaas, J., Botha, C.P., Post, F.H.: Interactive visualization of multi-field medical data using linked physical and feature-space views. In: EuroVis, pp. 123–130 (2007)Google Scholar
  9. 9.
    Daniels, J. II., Anderson, E.W., Nonato, L.G., Silva, C.T.: Interactive vector field feature identification. IEEE Trans. Vis. Comput. Graph. 16, 1560–1568 (2010)Google Scholar
  10. 10.
    Dobrev, P., Van Long, T., Linsen, L. : A cluster hierarchy-based volume rendering approach for interactive visual exploration of multi-variate volume data. In: Proceedings of 16th International Workshop on Vision, Modeling and Visualization (VMV 2011), pp. 137–144. Eurographics Association (2011)Google Scholar
  11. 11.
    Elmoasry, A., Maswadah, M.S., Linsen, L.: Semi-automatic rough classification of multichannel medical imaging data. In: Visualization in Medicine and Life Sciences II (to appear). Springer, Heidelberg (2011)Google Scholar
  12. 12.
    Faloutsos, C., Lin, K.I.: Fastmap: a fast algorithm for indexing, data-mining and visualization of traditional and multimedia datasets. In: SIGMOD ’95: Proceedings of the 1995 ACM SIGMOD international Conference on Management of data, pp. 163–174. ACM Press, New York (1995)Google Scholar
  13. 13.
    Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers, Los Altos (2006)Google Scholar
  14. 14.
    Heckbert, P.: Color image quantization for frame buffer display. In: Computer Graphics (Proceedings of ACM SIGGRAPH 82), pp. 297–307 (1982)Google Scholar
  15. 15.
    Heinrich, J., Bachthaler, S., Weiskopf, D.: Progressive splatting of continuous scatterplots and parallel coordinates. Comput. Graph. Forum 30(3), 653–662 (2011)CrossRefGoogle Scholar
  16. 16.
    Ivanovska, T: Efficient multichannel image partitioning: theory and application. PhD thesis, School of Engineering and Science, Jacobs University, Bremen, Germany (2009)Google Scholar
  17. 17.
    Ivanovska, T., Linsen, L: A user-friendly tool for semi-automated segmentation and surface extraction from color volume data using geometric feature space operations. In: Visualization in Medicine and Life Sciences, pp. 145–162, 319. Springer, Heidelberg (2007)Google Scholar
  18. 18.
    Jain, A.K., Dubes, R.C.: Algorithms for Clustering Data. Prentice Hall, Englewood Cliffs (1988)MATHGoogle Scholar
  19. 19.
    Jänicke, H., Wiebel, A., Scheuermann, G., Kollmann, W.: Multifield visualization using local statistical complexity. IEEE Trans. Vis. Comput. Graph. 13(6), 1384–1391 (2007)CrossRefGoogle Scholar
  20. 20.
    Kandogan, E.: Star coordinates: a multi-dimensional visualization technique with uniform treatment of dimensions. In: Proceedings of IEEE Information Visualization Symposium (Hot Topics), pp. 4–8 (2000)Google Scholar
  21. 21.
    Kindlmann, G., Durkin, J.W.: Semi-automatic generation of transfer functions for direct volume rendering. In: IEEE Symposium on Volume Visualization, pp. 79–86 (1998)Google Scholar
  22. 22.
    Kniss, J., Kindlmann, G., Hansen, C.: Interactive volume rendering using multi-dimensional transfer functions and direct manipulation widgets. In: VIS ’01: Proceedings of the Conference on Visualization ’01, pp. 255–262. IEEE Computer Society, Washington, DC, USA (2001)Google Scholar
  23. 23.
    Lehmann, D.J., Theisel, H.: Discontinuities in continuous scatter plots. IEEE Trans. Vis. Comput. Graph. 16, 1291–1300 (2010)CrossRefGoogle Scholar
  24. 24.
    Lehmann, D.J., Theisel, H.: Features in continuous parallel coordinates. IEEE Trans. Vis. Comput. Graph. 17(12), 1912–1921 (2011)CrossRefGoogle Scholar
  25. 25.
    Linsen, L., Van Long, T., Rosenthal, P., Rosswog, S.: Surface extraction from multi-field particle volume data using multi-dimensional cluster visualization. IEEE Tran. Vis. Comput. Graph. 14(6), 1483–1490 (2008)CrossRefGoogle Scholar
  26. 26.
    Linsen, L., Van Long, T., Rosenthal, P.: Linking multi-dimensional feature space cluster visualization to surface extraction from multi-field volume data. IEEE Comput. Graph. Appl. 29(3), 85–89 (2009)CrossRefGoogle Scholar
  27. 27.
    Van Long, T., Linsen, L.: MultiClusterTree: Interactive visual exploration of hierarchical clusters in multidimensional multivariate data. Comput. Graph. Forum 28(3), 823–830 (2009)CrossRefGoogle Scholar
  28. 28.
    Van Long, T., Linsen, L.: Visualizing high density clusters in multidimensional data using optimized star coordinates. Computat. Stat. 26, 655–678 (2011)CrossRefGoogle Scholar
  29. 29.
    Maciejewski, R., Woo, I., Chen, W., Ebert, D.: Structuring feature space: a non-parametric method for volumetric transfer function generation. IEEE Trans. Vis. Comput. Graph. 15(6), 1473–1480 (2009)CrossRefGoogle Scholar
  30. 30.
    MacQueen, J.B.: Some methods for classification and analysis of multivariate observations. In: Proceedings of 5-th Berkeley Symposium on Mathematical Statistics and Probability, vol. 1, pp. 281–297 (1967)Google Scholar
  31. 31.
    Paulovich, F.V., Minghim, R.: Text map explorer: a tool to create and explore document maps. In: Proocedings of the 10th International Conference on Information Visualisation—IV, pp. 245–251. IEEE CS Press. London (2006)Google Scholar
  32. 32.
    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
  33. 33.
    Paulovich, F.V., Silva, C., Nonato, L.G.: Two-phase mapping for projecting massive data sets. IEEE Trans. Vis. Comput. Graph. 16, 1281–1290 (2010)CrossRefGoogle Scholar
  34. 34.
    Sammon, J.W.: A nonlinear mapping for data structure analysis. IEEE Trans. Comput. 18(5), 401–409 (1969)CrossRefGoogle Scholar
  35. 35.
    Sips, M., Neubert, B., Lewis, J.P., Hanrahan, P.: Selecting good views of high-dimensional data using class consistency. Comput. Graph. Forum 28(3), 831–838 (2009)CrossRefGoogle Scholar
  36. 36.
    Takanashi, I., Lum, E.B., Ma, K.L., Muraki, S.: Ispace: interactive volume data classification techniques using independent component analysis. In: Pacific Conference on Computer Graphics and Applications, p. 366 (2002)Google Scholar
  37. 37.
    Tejada, E., Minghim, R., Nonato, L.G.: On improved projection techniques to support visual exploration of multidimensional data sets. Inf. Vis. 2(4), 218–231 (2003)Google Scholar
  38. 38.
    Tzeng, F.Y., Lum, E.B., Ma, K.L.: A novel interface for higher-dimensional classification of volume data. In: VIS ’03: Proceedings of the 14th IEEE Visualization 2003 (VIS’03), pp. 66. IEEE Computer Society, Washington, DC (2003)Google Scholar
  39. 39.
    Tzeng, F.Y., Ma, K.L.: A cluster-space visual interface for arbitrary dimensional classification of volume data. In: Proceedings of Joint Eurographics-IEEE TVCG Symposium on Visualization, pp 17–24 (2004)Google Scholar
  40. 40.
    Van Long, T.: Visualizing high-density clusters in multidimensional data. PhD thesis, School of Engineering and Science, Jacobs University, Bremen, Germany (2009)Google Scholar
  41. 41.
    Whalen, D. Norman, M.L.: Competition data set and description. In: IEEE Visualization Design Contest (2008). http://vis.computer.org/VisWeek2008/vis/contests.html

Copyright information

© Springer-Verlag London 2014

Authors and Affiliations

  1. 1.Jacobs UniversityBremenGermany

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