Advertisement

Assisting Human Cognition in Visual Data Mining

  • Simeon J. Simoff
  • Michael H. Böhlen
  • Arturas Mazeika
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4404)

Abstract

As discussed in Part 1 of the book in chapter “Form-Semantics-Function – A Framework for Designing Visualisation Models for Visual Data Mining” the development of consistent visualisation techniques requires systematic approach related to the tasks of the visual data mining process. Chapter “Visual discovery of network patterns of interaction between attributes” presents a methodology based on viewing visual data mining as a “reflection-in-action” process. This chapter follows the same perspective and focuses on the subjective bias that may appear in visual data mining. The work is motivated by the fact that visual, though very attractive, means also subjective, and non-experts are often left to utilise visualisation methods (as an understandable alternative to the highly complex statistical approaches) without the ability to understand their applicability and limitations. The chapter presents two strategies addressing the subjective bias: “guided cognition” and “validated cognition”, which result in two types of visual data mining techniques: interaction with visual data representations, mediated by statistical techniques, and validation of the hypotheses coming as an output of the visual analysis through another analytics method, respectively.

Keywords

Association Rule Density Level Cognitive Style Data Mining Algorithm Ventral Stream 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Wong, P.C.: Visual Data Mining. IEEE Computer Graphics and Applications, 1–3 (September/October, 1999)Google Scholar
  2. 2.
    Ankerst, M.: Visual Data Mining. Fakultät für Mathematik und Informatik. Ludwig-Maximilians-Universität, München (2000)Google Scholar
  3. 3.
    Schulz, H.-J., Nocke, T., Schumann, H.: A Framework for Visual Data Mining of Structures. In: Proceedings of the Twenty-Ninth Australasian Computer Science Conference (ACSC 2006). Conferences in Research and Practice in Information Technology, Hobart, Tasmania, Australia. CPRIT, vol. 48 (2006)Google Scholar
  4. 4.
    Witkin, H.A., Goodenough, D.R.: Cognitive Styles: Essence and Origins, vol. 141. International Universities Press, New York (1981)Google Scholar
  5. 5.
    Tufte, E.R.: Visual and Statistical Thinking: Displays of Evidence for Decision Making. Graphics Press (1997)Google Scholar
  6. 6.
    Tufte, E.R.: Visual Explanations: Images and Quantities, Evidence and Narrative, vol. 156. Graphics Press (1997)Google Scholar
  7. 7.
    Card, S.K., Mackinlay, J.D., Shneiderman, B. (eds.): Readings in Information Visualization: Using Vision to Think. Morgan Kaufmann, San Francisco (1999)Google Scholar
  8. 8.
    Spence, R.: Information Visualization. Addison-Wesley, Reading (2001)Google Scholar
  9. 9.
    Tufte, E.R.: The Cognitive Style of PowerPoint: Pitching Out Corrupts Within. Graphics Press (2003)Google Scholar
  10. 10.
    Kozhevnikov, M., Kossyln, S., Shephard, J.: Spatial versus object visualizers: a new characterization of visual cognitive style. Memory and Cognition 33(4), 710–726 (2005)Google Scholar
  11. 11.
    Hofmann, H., Siebes, A., Wilhelm, A.: Visualizing association rules with interactive mosaic plots. In: Proceedings of ACM SIGKDD Int. Conf. On Knowledge Discovery and Data Mining (KDD 2000). ACM Press, Boston (2000)Google Scholar
  12. 12.
    Ankerst, M., Grinstein, G., Keim, D.: Visual Data Mining: Background, Techniques, and Drug Discovery Applications. In: Tutorial Notes, ACM SIGKDD Int. Conf. On Knowledge Discovery and Data Mining (KDD 2002), Edmonton, Canada (2002)Google Scholar
  13. 13.
    Bohlen, M.H., et al.: A Triangular Reconstruction of Density Surfaces. In: Proceedings 3rd International Workshop on Visual Data Mining, Melbourne, Florida, USA, November 19, University of Technology Sydney (2003)Google Scholar
  14. 14.
    Simoff, S.J., Noirhomme-Fraiture, M., Böhlen, M.H.: Proceedings of the International Workshop on Visual Data Mining VDM@PKDD 2001, Freiburg, Germany (2001)Google Scholar
  15. 15.
    Simoff, S.J., Noirhomme-Fraiture, M., Böhlen, M.H. (eds.): Proceedings InternationalWorkshop on Visual Data Mining VDM@ECML/PKDD 2002, Helsinki, Finland (2002)Google Scholar
  16. 16.
    Simoff, S.J., et al. (eds.): Proceedings 3rd International Workshop on Visual Data Mining VDM@ICDM 2003, Melbourne, Florida, USA (2003)Google Scholar
  17. 17.
    Keim, D.A., Eick, S. (eds.): Proceedings of KDD-2001 Workshop on Visual Data Mining, San Francisco, California, USA (2001)Google Scholar
  18. 18.
    Barabasi, A.-L.: Linked: The New Science of Networks. Perseus Publishing (2002)Google Scholar
  19. 19.
    Sudweeks, F., Simoff, S.J.: Complementary explorative data analysis: The reconciliation of quantitative and qualitative principles. In: Jones, S. (ed.) Doing Internet Research, pp. 29–55. Sage Publications, Thousand Oaks (1999)Google Scholar
  20. 20.
    Riva, G., Galimberti, C.: Complementary Explorative Multilevel Data Analysis – CEMDA: A socio-cognitive model of data analysis for Internet research. In: Riva, G., Galimberti, C. (eds.) Towards CyberPsychology: Mind, Cognitions and Society in the Internet Age, pp. 19–35. IOS Press, Amsterdam (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Simeon J. Simoff
    • 1
    • 2
  • Michael H. Böhlen
    • 3
  • Arturas Mazeika
    • 3
  1. 1.School of Computing and Mathematics College of Heath and ScienceUniversity of Western SydneyAustralia
  2. 2.Faculty of Information TechnologyUniversity of TechnologySydneyAustralia
  3. 3.Faculty of Computer ScienceFree University of Bozen-BolzanoItaly

Personalised recommendations