Visual Analysis and Exploration of Relationships

  • Beth Hetzler
Part of the Information Science and Knowledge Management book series (ISKM, volume 3)


Relationships can provide a rich and powerful set of information and can be used to accomplish application goals, such as information retrieval and natural language processing. A growing trend in the information science community is the use of information visualization—taking advantage of people’s natural visual capabilities to perceive and understand complex information. This chapter explores how visualization and visual exploration can help users gain insight from known relationships and discover evidence of new relationships not previously anticipated.


Association Rule Visual Analysis IEEE Symposium Graphical Attribute Information Visualization 
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.


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  1. Ahlberg, C., & Shneiderman, B. (1994). Visual information seeking tight coupling of dynamic query filters with starfield displays. Proceedings on Human Factors in Computing Systems (SIGCHI ’94), 313–317.Google Scholar
  2. Ashcraft, M. (1989). Human Memory and Cognition. New York: Harper Collins.Google Scholar
  3. Bertrand-Gastaldy, S. (1986). Improved design of graphic displays in thesauri through technology and ergonomics. Journal of Documentation, 42, 225–251.CrossRefGoogle Scholar
  4. Brath, R. (1997). Concept demonstration: Metrics for effective information visualization. Proceedings of IEEE Symposium on Information Visualization (InfoVis ’97), 108–111.Google Scholar
  5. Brodbeck, D., Chalmers, M., Lunzer, A., & Cotture, P. (1997). Domesticating Bead: Adapting an information visualization system to a financial institution. Proceedings of IEEE Symposium on Information Visualization (InfoVis ’97), 73–80.Google Scholar
  6. Card, S., MacKinlay, J., & Shneiderman, B. (Eds.). (1999). Readings in Information Visualization. San Francisco: Morgan Kaufmann.Google Scholar
  7. Chen, C. (1999). Information Visualization and Virtual Environments. London: Springer Verlag.Google Scholar
  8. Chen, C., & Carr, L. (1999). Visualizing the evolution of a subject domain: A case study. Proceedings of IEEE Visualization ’99, 449–452, color plate 561.Google Scholar
  9. Chen, C., & Czerwinski, M. (1998). Latent semantics to spatial hypertext—An integrated approach. Proceedings of the Ninth ACM Conference on Hypertext and Hypermedia: Links, Objects, Time and Space—Structure in Hypermedia Systems, 77–86.Google Scholar
  10. Christ, R. (1975). Review and analysis of color coding research for visual displays. Human Factors, 17, 542–570.Google Scholar
  11. Cleveland, W., & McGill, R. (1984). Graphical perception: Theory, experimentation, and application to the development of graphical methods. Journal of the American Statistical Association, 79, 531–554.CrossRefMathSciNetGoogle Scholar
  12. Fairchild, K., Poltrock, S., & Furnas, G. (1988). Sem Net: Three-dimensional graphic representation of large knowledge bases. In R. Guindon (Ed.), Cognitive Science and its Applications for Human-Computer Interaction. Hillsdale, NJ: Erlbaum.Google Scholar
  13. Havre, S., Hetzler, B., & Nowell, L. (2000). ThemeRiver: Visualizing theme changes over time. Proceedings of IEEE Symposium on Information Visualization (InfoVis 2000), 115–123.Google Scholar
  14. Hearst, M., & Karadi, C. (1997). Cat-a-Cone: An interactive interface for specifying searches and viewing retrieval results using a large category hierarchy. Proceedings of the 20th Annuallnternational ACMSIGIR Conference on Research and Development in Information Retrieval, 246–255.Google Scholar
  15. Hetzler, B., Harris, M., Havre, S., & Whitney, P. (1998). Visualizing the full spectrum of document relationships. Structures and Relations in Knowledge Organization, Proceedings of the Fifth International ISKO Conference, 168–175.Google Scholar
  16. Hoffman, D. (1998). Visual Intelligence. New York: W. W. Norton.Google Scholar
  17. Jeong, C., & Pang, A. (1998). Reconfigurable disc trees for visualizing large hierarchical information space. Proceedings of IEEElnformation Visualization (InfoVis ’98), 19–25.Google Scholar
  18. Johnson, B., & Shneiderman, B. (1991). Tree-maps: A space filling approach to the visualization of hierarchical information structures. Proceedings of IEEE Information Visualization Conference, 284–291.Google Scholar
  19. Kohonen, T. (1997). Exploration of very large databases by self-organizing maps. Proceedings of the 1997 IEEE International Conference on Neural Networks (ICNN ’97), PL1–PL3.CrossRefGoogle Scholar
  20. Kumar, V., & Furuta, R. (1999). Visualization of relationships. Proceedings of the Tenth ACM Conference on Hypertext and Hypermedia, 137–138.Google Scholar
  21. Lakoff, G., & Johnson, M. (1980). Metaphors We Live By. Chicago: University of Chicago Press.Google Scholar
  22. Lamping, J., & Rao, R. (1996). The hyperbolic browser: A focus + context technique for visualizing large hierarchies. Journal of Visual Languages and Computing, 7, 35–55.CrossRefGoogle Scholar
  23. Lancaster, F. (1972). Vocabulary Control for Information Retrieval. Washington: Information Resources Press.Google Scholar
  24. Lin, X. (1997). Map displays for information retrieval. Journal of the American Society for Information Science, 48, 40–54.CrossRefGoogle Scholar
  25. Mackay, W., & Beaudouin-Lafon, M. (1998). DIVA: Exploratory data analysis with multimedia streams. Proceedings on Human Factors in Computing Systems (SIGCHI ’98), 416–423.Google Scholar
  26. Mackinlay, J., Rao, R., & Card, S. (1995). An organic user interface for searching citation links. Proceedings on Human Factors in Computing Systems (SIGCHI ’95), 67–73.Google Scholar
  27. Michotte, A. (1963). The Perception of Causality. (T. Miles & E. Miles, Trans). New York: Basic Books.Google Scholar
  28. Munzner, T. (1997). H3 : Laying out large directed graphs in 3D hyperbolic space. Proceedings of IEEE Symposium on Information Visualization (InfoVis ’97), 2–10.Google Scholar
  29. Nowell, L. (1997). Graphical Encoding for Information Visualization: Using Icon Color, Shape, and Size to Encode Nominal and Quantitative Data. Doctoral dissertation, Virginia Tech. Available: < > [2001, October 9].Google Scholar
  30. Nowell, L., France, R., Hix, D., Heath, L., & Fox, E. (1996). Visualizing search results: some alternatives to query-document similarity. Proceedings of the 19th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 67–75.Google Scholar
  31. Plaisant, C., Heller, D., Li, J., Shneiderman, B., Mushinlin, R., & Karat, J. (1998). Visualizing medical records with LifeLines. Summary from Human Factors in Computing Systems (SIGCHI ’98), 28–29.Google Scholar
  32. Plaisant, C., Milash, B., Rose, A., Widoff, S., & Shneiderman, B. (1996). Lifelines: visualizing personal histories. Proceedings on Human Factors in Computing Systems (SIGCHI ’96), 221–227.Google Scholar
  33. Risch, J., Rex, D., Dowson, S., Walters, T., May, R., & Moon, B. (1997). The STARLIGHT information visualization system. Proceedings 1997 IEEE International Conference on Information Visualization (IV ’97), 42–49.Google Scholar
  34. Rohrer, R., Ebert, D., & Sibert, J. (1998). The shape of Shakespeare: Visualizing text using implicit surfaces.Proceedings of IEEE Symposium on Information Visualization (InfoVis ’98), 121–129, color plate 160.Google Scholar
  35. Seber, G. (1984). Multivariate Observations. New York: John Wiley & Sons.zbMATHCrossRefGoogle Scholar
  36. SGI. (1999). Silicon Graphics Mine Set TM Supporting the Discovery Research Process. Available: < > [2001, October 9].Google Scholar
  37. Shneiderman, B. (1999). Crossing the information visualization chasm: From innovation to adoption. Keynote address to IEEE Symposium on Information Visualization (InfoVis ’99).Google Scholar
  38. Small, H. (1999). Visualizing science by citation mapping. Journal of the American Society for Information Science. 50, 799–813.CrossRefGoogle Scholar
  39. Sprenger, T. C., Gross, M. H., Bielser, D., Strasser, T. (1998). IVORY: An object-oriented framework for physics-based information visualization in Java. Proceedings of IEEE Symposium on Information Visualization (InfoVis ’98), 79–86, color plate 155.Google Scholar
  40. Stasko, J., & Zhang, E. (2000). Focus+context display and navigation techniques for enhancing radial, space-filling hierarchy visualizations. Proceedings of IEEE Symposium on Information Visualization (InfoVis 2000), 57–65.Google Scholar
  41. Swanson, Don R. (1993). Intervening in the life-cycles of scientific knowledge. Library Trends, 41, 606–631.Google Scholar
  42. Technisch Documentatie-en Informatie-Centrum voor de Krijgsmacht [TDCK]. (1963). TDCK Circular Thesaurus System. The Hague: TDCK.Google Scholar
  43. Thomas, J., Cook, K., Crow, V., Hetzler, B., May, R., McQuerry, D., McVeety, R., Miller, N., Nakamura, G., Nowell, L., Whitney, P., & Wong, P. (2000). Human computer interaction with global information spaces—Beyond data mining. In J. Vince & R. Earnshaw (Eds.), Digital Media: The Future, 32–46. London: Springer Verlag.Google Scholar
  44. Tufte, E. (1997). Visual Explanations: Images and Quantities, Evidence and Narrative. Cheshire, CT: Graphics Press.zbMATHGoogle Scholar
  45. Ware, C., Neufeld, E., & Bartram, L. (1999). Visualizing causal relations. Proceedings of Late Breaking Hot Topics/IEEE Symposium on Information Visualization (InfoVis ’99), 39–42.Google Scholar
  46. Wills, G. & Dill, J. (Eds). (1998). Proceedings of IEEE Symposium on Information Visualization (InfoVis ’98).Google Scholar
  47. Wills, G. & Keim, D. (Eds). (1999). Proceedings of IEEE Symposium on Information Visualization (InfoVis ’99).Google Scholar
  48. Wise, J., Thomas, J., Pennock, K., Lantrip, D., Pottier, M., Schur, A., & Crow, V. (1995). Visualizing the non-visual: Spatial analysis & interaction with information from text documents. Proceedings of IEEE Symposium on Information Visualization (InfoVis ’95), 51–58, color plate 140.Google Scholar
  49. Wong, P., Whitney, P., & Thomas, J. (1999). Visualizing association rules for text mining. Proceedings of IEEE Symposium on Information Visualization (InfoVis ’99), 120–123, color plate 152.Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2002

Authors and Affiliations

  • Beth Hetzler
    • 1
  1. 1.Pacific Northwest National LaboratoryRichlandUSA

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