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Issue No.05 - September/October (1999 vol.19)
pp: 40-46
Visual data mining requires a tightly coupled visual interface with underlying information retrieval and analysis engines. To be useful, these techniques must augment human exploration and discovery beyond existing methods. Since the focus of data mining is to <p>provide humans with supporting tools to think and explore, human perceptual issues are an important component of effective visual interfaces for such systems.</p> <p>In this article we describe a shape-based visual interface for information retrieval and interactive exploration. Our exploratory system uses procedurally generated shapes coupled with an underlying text retrieval engine. Traditional text-based queries and summarization are enhanced with a visual interface based on 3D shapes (glyphs). Our interface allows visualizing multidimensional relationships among documents and perceiving more information than with conventional text-based interfaces. It promotes information overview and "drill-down" in support of analysis. Before describing our visual interface and application, we introduce information retrieval within the context of data mining and provide a brief over-view of procedural shape generation. We then describe our current system and give a few relevant examples. Finally, we offer some ideas for future enhancements and direction.</p>
Randall M. Rohrer, John L. Sibert, David S. Ebert, "A Shape-Based Visual Interface for Text Retrieval", IEEE Computer Graphics and Applications, vol.19, no. 5, pp. 40-46, September/October 1999, doi:10.1109/38.788797
1. S.K. Card, J.D. Mackinlay, and B. Shneiderman, Readings in Information Visualization, Morgan Kaufmann, San Francisco, 1999.
2. E. Riloff and L. Hollar, "Text Databases and Information Retrieval," The Computer Science and Engineering Handbook (chapter 50), A.B. Tucker Jr., ed., CRC Press, Boca Raton, Fla., 1997.
3. G. Salton and M. McGill, Introduction to Modern Information Retrieval, McGraw Hill, New York, 1983.
4. G. Kowalski, Information Retrieval Systems: Theory and Implementation, Kluwer Academic Publishers, Boston, 1997.
5. D. Ebert, "Advanced Geometric Modeling," The Computer Science and Engineering Handbook (chapter 56), A.B. Tucker Jr., ed, CRC Press, Boca Raton, Fla., 1997.
6. D. Ebert et al., "Procedural Shape Generation for Multidimensional Data Visualization," Data Visualization 99: Proc. of the Joint Eurographics-IEEE TCVG Symp. on Visualization, Springer-Verlag, New York, May 1999, pp. 3-12.
7. R. Rohrer, D. Ebert, and J. Sibert, "The Shape of Shakespeare: Visualizing Text using Implicit Surfaces," Proc. IEEE Symp. on Information Visualization 98, IEEE Computer Society Press, Los Alamitos, Calif., 1998, pp. 121-129.
8. J. Bloomenthal et al., Introduction to Implicit Surfaces, J. Bloomenthal, ed., Morgan Kaufmann, San Francisco, 1997.
9. S. Mukherjea, K. Hirata, and Y. Hara, "Visualizing the Results of Multimedia Web Search Engines," Proc. IEEE Symp. on Information Visualization 96, IEEE CS Press, Los Alamitos, Calif., 1996, pp. 64-65.
10. M.A. Hearst, "TileBars: Visualization of Term Distribution Information in Full Text Information Access," Proc. ACM Conf. on Human Factors in Computing Systems (CHI 95), ACM Press, New York, 1995, pp. 59-66.
11. A. Veerasamy and N.J. Belkin, "Evaluation of a Tool for Visualization of Information Retrieval Results," Proc. ACM Conf. on Research and Development in Information Retrieval (SIGIR 96), ACM Press, New York, pp. 85-92.
12. Smart Information Retrieval System, Cornell University, Ithaca, N.Y.,
13. J. Bloomenthal, "An Implicit Surface Polygonizer," Graphics Gems IV, A. Paeth, ed., Academic Press, Chestnut Hill, Mass., 1994, pp. 324-349.
14. E. Chi et al., "A Spreadsheet Approach to Information Visualization," Proc. IEEE Symp. on Information Visualization 97, IEEE CS Press, Los Alamitos, Calif., 1997, pp. 17-24.
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