Knowledge visualization techniques for machine learning

Intelligent Data Analysis

Volumn 2, Issue 4, 1998, Pages 333-347

  Humphrey, Matt ^a   Cunningham, Sally Jo ^a   Witten, Ian H ^a  

^a UNIVERSITY OF WAIKATO   (New Zealand)

Author keywords

Decision graph; Decision tree; Graphical visualization; Knowledge representation

Indexed keywords

DATA SET; DECISION GRAPHS; EMPIRICAL DATA; END USERS; GRAPHICAL REPRESENTATIONS; GRAPHICAL VISUALIZATION; KNOWLEDGE VISUALIZATION; PRODUCTION RULES;

KNOWLEDGE REPRESENTATION; LEARNING SYSTEMS; VISUALIZATION;

DECISION TREES;

EID: 0003496503     PISSN: 1088467X     EISSN: 15714128     Source Type: Journal    
DOI: 10.3233/IDA-1998-2406     Document Type: Article

References (19)

1. Bertin, 1, Semiology of Graphics, translated by W.J. Berg, University of Wisconsin Press, Milwaukee, Wisconsin, USA, 1983.
2. Becker, RA., Cleveland, W.S. Brushing Scatterplots, Technometrics, 29, 2, 1987.
3. Casner, S., A Task-analytic approach to the automated design of graphic presentations, ACM Transactions of Graphics, 10, 111-l5 1991.
4. Catlett, J., Rule induction as exploratory data analysis, in Proceedings of the IJCAI'95 Workshop on Machine Learning and Comprehensibility, 1995. Available at . http://www.lri.fr/cnlweb-ijcailjason.ps.
5. COITuble, v., Thire, F., and Ganascia, J.-G., Comprehensible exploratory induction with decision graphs, in Proceedings of the IJCAI'95 Workshop on Machine Learning and Comprehensibility, 1995. Available at . http://www.lri.fr/cnlwebijcailvincent.ps.
6. Goodenough, J. and Gerhart, S., Toward a theory of test data selection, IEEE Transactions on Software Engineering. SE-l, 2,156-173,1975.
7. Gregory, G., Analysis, Pitman Publishing, London, 1988.
8. Halverson, R, An empirical investigation comparing IF-THEN rules and decision tables for programming rule-based expert systems, in Proceedings of the 26th Hawaii International Conference on System Sciences, vol. III, 316.
9. Humphrey, M.e., A Graphical Notation for the Design ofInforrnation Visualisations, D.Phil Thesis, University ofWaikato, Hamilton, New Zealand, 1996.
10. Kohavi, R, The power of decision tables, in Proceedings of the European Conference on Machine Learning (ECML), 1995. Available at . ftp://starry. stanford.edulpub/ronnykitables.ps.
11. Leung, Y.K., Apperley, M.D., E3: Towards the Metrication of Graphical Presentation Techniques for Large Data Sets, In Lecture Notes in Computer Science 75 (Goos & Hartmanis, eds.), Springer-Verlag, Berlin, 125.
12. Lew, A., Decision tables for general-purpose scientific programming, Software Practice and Experience, l3, 181-188, 1983.
13. Lew, A., Proof of correctness of decision table programs, Computer Journal, 27, 3, 230-232, 1984.
14. Maes, R, and van Kijk, J., On the role of ambiguity and incompleteness in the design of decision tables and rule-based systems, Computer Journal, 31,6,481-489,1988.
15. Oliver, L.L., Decision graphs-an extension of decision trees, in Proceedings of the 4th International Workshop on AI and Statistics, by the Society for Artifiial Intelligence and Statistics and the International Association for Statistical Computing, 343-350, January 1993.
16. Roth, S., Mattis, J., Data Characterization for Intelligent Graphics Presentation, in SIGCHI Bulletin, 193-200, April 1993.
17. Sparrow, L.A., Graphical displays in Information Systems: some data properties influencing the effectiveness of alternative forms, Behaviour and Information Technology, 8, 1,43-56, 1989.
18. Stone, H. S., Discrete Mathematical Structures and Their Applications, SRA Computer Science Series, Science Research Associates, 1973.
19. Subramanian, G.H., Nosek, J., Raghunathan, S.P. and Kanitkar, S.S., A comparison of the decision table and tree, Communications of the ACM, 35,1,89-94, 1992.