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Volume 41, Number 3, 2002
Artificial Intelligence
 Table of contents: arrowHTML arrowPDF arrowASCII   This article: arrowHTML arrowPDF arrowASCII arrowCopyright info
   

Applying machine learning to automated information graphics generation - References

by M. X. Zhou, S. Ma, and Y. Feng

Cited references and notes

  1. G. Robertson, S. Card, and J. MacKinlay, “Information Visualization Using 3D Interactive Animation,” Communications of the ACM 36, No. 4, 56–71 (1993).
  2. J. Mackinlay, “Automating the Design of Graphical Presentations of Relational Information,” ACM Transactions on Graphics 5, No. 2, 110–141 (1986).
  3. S. F. Roth and J. Mattis, “Automating the Presentation of Information,” Proceedings, 7th IEEE Conference on AI Applications, Miami Beach, FL (February 26–28, 1991), pp. 90–97.
  4. M. Zhou and S. Feiner, “Efficiently Planning Coherent Visual Discourse,” Journal of Knowledge-Based Systems 10, No. 5, 275–286 (March 1998).
  5. M. Chuah, S. Roth, and S. Kerpedjiev, “Sketching, Searching, and Customizing Visualizations: A Content-Based Approach to Design Retrieval,” Intelligent Multimedia Information Retrieval, M. Maybury, Editor, AAAI Press/The MIT Press, Cambridge, MA (1997), pp. 83–111.
  6. M. Derthick and S. Roth, “Example-Based Generation of Custom Data Analysis Appliances,” Proceedings, International Conference on Intelligent User Interfaces, Santa Fe, NM (January 14–17, 2001).
  7. S. Pan and K. McKeown, “Learning Intonation Rules for Concept to Speech Generation,” Proceedings, 17th International Conference on Computational Linguistics and 36th Annual Meeting of the American Association for Computational Linguistics, Volume 2, Montreal, Canada (August 10–14, 1998).
  8. S. F. Roth and J. Mattis, “Data Characterization for Intelligent Graphics Presentation,” Proceedings, ACM Conference on Human Factors in Computing Systems, Seattle, WA (April 1–5, 1990), pp. 193–200.
  9. M. Zhou and S. Feiner, “Automated Visual Presentation: From Heterogeneous Information to Coherent Visual Discourse,” Journal of Intelligent Information Systems 11, 205–234 (1998).
  10. S. Casner, “A Task-Analytic Approach to the Automated Design of Graphic Presentations,” ACM Transactions on Graphics 10, No. 2, 111–151 (1991).
  11. G. Lohse, K. Biolsi, and H. Rueter, “A Classification of Visual Representations,” Communications of the ACM 37, No. 12, 36–49 (1994).
  12. J. Marks, “A Formal Specification Scheme for Network Diagrams that Facilitates Automated Design,” Journal of Visual Languages and Computing 2, No. 4, 395–414 (1991).
  13. B. Myers, S. Hudson, and R. Pausch, “Past, Present, and Future of User Interface Software Tools, ACM Transactions on Computer-Human Interactions 7, No. 1, 3–28 (2000).
  14. B. Myers, J. Goldstein, and M. Goldberg, “Creating Charts by Demonstration,” Proceedings, ACM Conference on Human Factors in Computing Systems, Boston, MA (April 24–28, 1994), pp. 106–111.
  15. M. Zhou, “Visual Planning: A Practical Approach to Automated Visual Presentation,” Proceedings, International Joint Conference on Artificial Intelligence, Stockholm, Sweden (July 31–August 6, 1999), pp. 634–641.
  16. M. Zhou and S. Ma, “Toward Applying Machine Learning to Design Rule Acquisition for Automated Graphics Generation,” Proceedings, AAAI Spring Symposium Series on Smart Graphics, Stanford, CA (March 20–22, 2000).
  17. M. Zhou and S. Ma, “Representing and Retrieving Visual Presentations for Example-Based Graphics Generation,” Proceedings, 1st International Symposium on Smart Graphics, Hawthorne, NY (March 21–23, 2001), pp. 87–94.
  18. A visual composition is a graph, rather than a tree, because the children might be related to each other, and one child may have multiple parents.
  19. See more at http://www.research.ibm.com/RIA/Improvise/Improvise.htm.
  20. Y. Aslandogan, C. Their, C. Yu, and N. Rishe, “Using Semantic Contents and Wordnet in Image Retrieval,” Proceedings, 20th International ACM SIGIR Conference on Research and Development in Information Retrieval, Philadelphia, PA (July 27–31, 1997), pp. 286–295.
  21. T. Mitchell, Machine Learning, McGraw-Hill, New York (1997).
  22. S. Ma and C. Ji, “Performance and Efficiency: Recent Advances in Supervised Learning,” Proceedings of the IEEE 87, No. 9, 1519–1536 (1999).
  23. L. Breiman, J. H. Friedman, and C. J. Stone, Classification and Regression Trees, Wadsworth Publishing Company, Belmont, CA (1984).
  24. J. Quinlan, C4.5: Programs for Machine Learning, Morgan Kaufman Publishers, San Francisco, CA (1993).
  25. P. R. Keller and M. M. Keller, Visual Cues: Practical Data Visualization, IEEE Computer Society Press, Los Alamitos, CA (1993).
  26. R. Wurman, Information Architects, Graphics Press, New York (1996).
  27. P. Wildbur and M. Burke, Information Graphics: Innovative Solutions in Contemporary Design, Thames & Hudson, London (1998).
  28. For simplicity, if a picture object contains more than one visual/data object, we treat it as one complex visual/data object.
  29. The composition relation within a picture object is the visual/data composition relation between the visual/data object and its children.
  30. The visual/data structure feature is excluded here, since it is already implied by the structure of the PVGraph and the PDGraph.
  31. R. Duda and P. Hart, Pattern Classification and Scene Analysis, John Wiley & Sons, Inc., New York (1973).
  32. See http://www.xmlspy.com.
  33. R. Harris, Information Graphics: A Comprehensive Illustrated Reference, Management Graphics, Minneapolis, MN (1996).
  34. In fact it is common to use the same visual cue to encode the same type of data for different data instances to achieve visual consistency. But it causes visual confusion if the same cue is used for two different types of data.