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Dynamic Decision Tree for Data Mining
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 Title & Authors
Dynamic Decision Tree for Data Mining
Choi, Byong-Su; Cha, Woon-Ock;
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 Abstract
Decision tree is a typical tool for data classification. This tool is implemented in DAVIS (Huh and Song, 2002). All the visualization tools and statistical clustering tools implemented in DAVIS can communicate with the decision tree. This paper presents methods to apply data visualization techniques to the decision tree using a real data set.
 Keywords
Decision tree;cluster analysis;data visualization;DAVIS;
 Language
Korean
 Cited by
 References
1.
Breiman, L., Friedman, J. H., Olshen, R. A. and Stone, C. J. (1984). Classification and Regression Trees, Monterey, CA: Wadsworth & Brooks/Cole Advanced Books & Software

2.
Cleveland, W. S. and McGill, M. E. (1988). Dynamic Graphics for Statistics, Wadsworth & Brooks/Cole, Belmont, CA

3.
Huh, M. Y. (2001). Strategy for visual clustering, The Korean Journal of Applied Statistics, 4, 177–190. (in Korean)

4.
Huh, M. Y. (1995). Exploring multidimensional data with FEDF, Journal of Computational and Graphical Statistics, 4, 335–343

5.
Huh, M. Y. (2009). http://stat.skku.ac.kr/myhuh.

6.
Huh, M. Y. and Song, K. Y. (2002). DAVIS: A Java-based data visualization system, Computational Statistics, 17, 411–423

7.
Kass, G. V. (1980) An exploratory technique for investigating large quantities of categorical data, Applied Statistics, 29, 119–127

8.
Nocholas, C. J. (1999). The emergence of data visualization and prospects for its business application, Masters of Information Systems Management Professional Seminar

9.
Quinlan, J. R. ( 1986). Induction of decision trees, Machine Learning, 1, 81–106 crossref(new window)

10.
Quinlan, J. R. (1993). C4.5: Programs for Machine Learning, Morgan Kaufmann Publishers

11.
Quinlan, J. R. (1996). Improved use of continuous attributes in c4.5., Journal of Artificial Intelligence Research, 4, 77–90

12.
Witten, I. H. and Frank, E. (2005). Data Mining: Practical Machine Learning Tools and Techniques, (Second Edition), Morgan Kaufmann