Skip to main content
Log in

Visualisierung und Analyse multidimensionaler Datensätze

  • HAUPTBEITRAG
  • MULTIDIMENSIONALE DATENSÄTZE
  • Published:
Informatik-Spektrum Aims and scope

Zusammenfassung

Für multidimensionale Datensätze existieren eine Reihe von automatischen Analysemethoden und Visualisierungstechniken, um ihnen innewohnende Zusammenhänge und Charakteristika aufzudecken. Die zunehmende Größe und Komplexität solcher Daten macht es notwendig, beide Ansätze miteinander zu kombinieren. In diesem Artikel stellen wir Ihnen daher etablierte Methoden zur visuellen und zur automatischen Datenanalyse vor und zeigen neuere Ansätze auf, diese sinnvoll miteinander zu kombinieren. Dabei werden alle Erläuterungen anhand anschaulicher Beispiele verdeutlicht und so für den Leser nachvollziehbar.

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

Access this article

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

Instant access to the full article PDF.

References

  1. Albuquerque G, Eisemann M, Lehmann DJ, Theisel H, and Magnor M (2009) Quality-based visualization matrices. Proceedings of Vision, Modeling, and Visualization. Braunschweig

  2. Asimov D (1985) The grand tour: a tool for viewing multidimensional data. J Sci Stat Comp 6(1):128–143

    Article  MATH  MathSciNet  Google Scholar 

  3. Bachthaler S, Weiskopf D (2008) Continuous Scatterplots. IEEE T Vis Comput Gr 16(6):1428–1435

    Article  Google Scholar 

  4. Beyer SK, Goldstein J, Ramakrishnan R, Shaft U (1999) When is “nearest neighbor” meaningful? In: ICDT ’99: Proceedings of the 7th International Conference on Database Theory, London, UK, pp 217–235, Springer

  5. Cleveland SW (1993) Visualizing Data. Hobart Press, Summit, NJ

    Google Scholar 

  6. Everitt SB, Dunn G (1991) Applied Multivariate Data Analysis. Arnold

  7. Fisherkeller AM, Friedman HJ, Tukey WJ (1987) Prim-9: an interactive multi-dimensional data display and analysis system. In: Sleveland WS (ed) Dynamic Graphics for Statistics. Chapman and Hall, New York

    Google Scholar 

  8. Friedman HJ (1987) Exploratory projection pursuit. J Am Stat Assoc 82:249–266

    Article  MATH  Google Scholar 

  9. Heinrich J, Weiskopf D (2009) Continuous Parallel Coordinates. IEEE T Vis Comput Gr (Proceedings Visualization/Information Visualization 2009) 15(6):1531–1538

    Article  Google Scholar 

  10. Hinneburg A, Aggarwal CC, Keim AD (2000) What is the nearest neighbor in high dimensional spaces? In: VLDB ’00: Proceedings of the 26th International Conference on Very Large Data Bases, San Francisco, CA, USA, pp 506–515, Morgan Kaufmann Publishers Inc.

  11. Hoffman P, Grinstein G, Marx K, Grosse I, and Stanley E (1997) Dna visual and analytic data mining. In: Proceedings of the 8th conference on Visualization, Phoenix, AZ, pp 437ff

  12. Inselberg A (2009) Parallel Coordinates. Springer, Berlin

    Book  MATH  Google Scholar 

  13. Johansson S, Johansson J (2009) Interactive dimensionality reduction through user-defined combinations of quality metrics. IEEE T Vis Comput Gr 15(6):993–1000

    Article  MathSciNet  Google Scholar 

  14. Keim D, Ankerst M, Kriegel H (1995) Recursive pattern: a technique for visualizing very large amounts of data. In: Proc. Visualization 1995 IEEE Computer Society Press, Washington, DC, pp 279–287

  15. Kohonen T (1995) Self Organizing Maps. Springer

  16. Mead A (1992) Review of the development of multidimensional scaling methods, vol 33. The Statistician 41:27–39

    Article  Google Scholar 

  17. Moore SD, McCabe PG (1999) Introduction to the Practice of Statistics. WH Freeman, New York, NY

    Google Scholar 

  18. Nocke T (2007) Visuelles Data Mining und Visualisierungsdesign für die Klimaforschung. Dissertation, Universität Rostock, Fakultät für Informatik und Elektrotechnik

  19. Picket MR, Grindstein G (1988) Iconographics displays for visualizing multidimensional data. In: Proc. IEEE Conference on Systems, Man and Cybernetics, Beijing and Shenyang, pp 514–519

  20. Rao R, Card KS (1994) The table lens: merging graphical and symbolic representations in an interactive focus+context visualization for tabular information. In: Proceedings of the ACM SIGCHI Conference on Human Factors in Computing Systems, pp 318–322

  21. Schumann H, Müller W (2000) Visualisierung: Grundlagen und allgemeine Methoden. Springer

  22. Seo J, Shneiderman B (2005) A rank-by-feature framework for interactive exploration of multidimensional data. Inform Visual 4(2):96–113

    Article  Google Scholar 

  23. Sips M, Neubert B, Lewis PJ, Hanrahan P (2009) Selecting good views of high-dimensional data using class consistency. Comput Graph Forum (Proc. EuroVis 2009) 28(3):831–838

    Article  Google Scholar 

  24. Tatu A, Albuquerque G, Eisemann M, Schneidewind J, Theisel H, Magnor M, Keim D (2009) Combining automated analysis and visualization techniques for effective exploration of high dimensional data. In: IEEE Symposium on Visual Analytics Science and Technology, New Jersey, pp 59–66

  25. Thomas JJ, Cook KA (2006) A Visual Analytics Agenda. IEEE Comput Graph 10–13

  26. Wattenberg M (2005) A note on space-filling visualizations and space-filling curves. In: Proc. of the 2005 IEEE Symposium on Information Visualization, pp 181–186

  27. Wong PC, Bergeron RD (1997) 30 Years of Multidimensional Multivariate Visualization. In: Scientific Visualization, Overviews, Methodologies, and Techniques. IEEE Computer Society Press, Washington, DC, pp 3–33

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Dirk J. Lehmann.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Lehmann, D., Albuquerque, G., Eisemann, M. et al. Visualisierung und Analyse multidimensionaler Datensätze. Informatik Spektrum 33, 589–600 (2010). https://doi.org/10.1007/s00287-010-0481-z

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00287-010-0481-z

Navigation