The use of a uniform declarative model in 3D visualisation for case-based reasoning

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 2416, Issue , 2002, Pages 103-117

  Falkman, Göran ^a  

^a UNIVERSITY OF SKÖVDE   (Sweden)

Author keywords

[No Author keywords available]

Indexed keywords

3D MODELING; GEOMETRY; PATTERN RECOGNITION; VISUALIZATION;

3D VISUALISATION; CASEBASED REASONINGS (CBR); DECLARATIVE MODELS; INFORMATION VISUALISATION; INTERACTIVE VISUALISATION; PARALLEL COORDINATE PLOTS; PARALLEL COORDINATES; SIMILARITY ASSESSMENT;

CASE BASED REASONING;

EID: 84942756790     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-46119-1_9     Document Type: Conference Paper

References (19)

1. D. W. Aha and H. Munos-Avila. Applied intelligence: Special issue on interactive case-based reasoning. Applied Intelligence, 14(1):1–2, 2001.
2. M. Ankerst, S. Berchtold, and D. A. Keim. Similarity clustering of dimensions for an enhanced visualization of multidimensional data. In G. Wills and J. Dill, editors, Proceedings of the IEEE Symposium on Information Visualization (InfoVis’98), October 19–20, 1998, Research Triangle Park, North Carolina, USA, pages 52–59, Los Alamitos, CA, USA, 1998. IEEE Computer Society Press.
3. L. Chittaro. Information visualization and its application to medicine. Artificial Intelligence in Medicine, 22(2):81–88, 2001.
4. T. Chomut. Exploratory data analysis in parallel coordinates. Research report, IBM Los Angeles Scientific Center, 1987.
5. G. Falkman. Similarity measures for structured representations: A definitional approach. In E. Blanzieri and L. Portinale, editors, Advances in Case-Based Reasoning. Proceedings of the 5th European Workshop, EWCBR 2000, Trento, Italy, September 6–9, 2000, volume 1898 of Lecture Notes in Artificial Intelligence, pages 380–392. Springer-Verlag, 2000.
6. G. Falkman. Information visualization in clinical odontology: Multidimensional analysis and interactive data exploration. Artificial Intelligence in Medicine, 22(2):133–158, 2001.
7. J. Fechter, T. Grunert, L. M. Encarnacao, and W. Straßer. User-centered development of medical visualization applications: Flexible interaction through communicating application objects. Computers & Graphics: Special Issue on Medical Visualization, 20(6):763–774, 1996.
8. A. J. W. Goldschmidt, C. J. Luz, W. Giere, R. Ludecke, and D. Jonas. Multidimensional visualization of laboratory findings and functional test-results for analyzing the clinical course of disease in medicine. Methods of Information in Medicine, 34(3):302–308, 1995.
9. L. Hallnas. Partial inductive definitions. Theoretical Computer Science, 87(1):115–142, 1991.
10. A. Inselberg. The plane with parallel coordinates. The Visual Computer, 1:69–91, 1985.
11. B. McCormick, T. A. DeFanti, and M. D. Brown. Visualization in scientific computing. Computer Graphics, 21(6), 1987.
12. E. McKenna and B. Smyth. An interactive visualisation tool for case-based reasoners. Applied Intelligence, 14(1):95–114, 2001.
13. C. L. North and B. Shneiderman. Snap-together visualization: Can users construct and operate coordinated views? Int. J. of Human-Computer Studies, 53(5):715–739, 2000.
14. C. L. North and B. Shneiderman. Component-based, user-constructed, multipleview visualization. In CHI 2001 Video Program. ACM Press, 2001.
15. B. Shneiderman. Designing the User Interface: Strategies for Effective Human- Computer Interaction. Addison-Wesley Longman, Inc., Reading, MA, USA, 3 edition, 1998.
16. H. Siirtola. Direct manipulation of parallel coordinates. In E. Banissi, M. Bannatyne, C. Chen, F. Khosrowshahi, and A. Ursyn, editors, Proceedings of the IEEE International Conference on Information Visualisation (IV 2000), pages 373–378. IEEE Computer Society Press, 2000.
17. R. Spence. Information Visualization. Addison-Wesley, Harlow, England, 2001.
18. J. W. Tukey. Exploratory Data Analysis. Addison-Wesley, Reading, MA, USA, 1977.
19. Q. Yang and J. Wu. Enhancing the effectiveness of interactive case-based reasoning with clustering and decision forests. Applied Intelligence, 14(1):49–64, 2001.