Abstract
This paper discusses a visualization technique integrated with inductive generalization. The technique represents classification rules inferred from data, as landscapes of graphical objects in a 3D visualization space, which can provide valuable insights into knowledge discovery and model-building processes. Such visual organization of classification rules can contribute to additional human insights into classification models that are hard to attain using traditional displays. It also includes navigational locomotion and high interactivity to facilitate the interpretation and comparison of results obtained in various classification scenarios. This is especially apparent for large rule sets where browsing through textual syntax of thousands of rules is beyond human comprehension. Visualization of both knowledge and data aids in assessing data quality and provides the capability for data cleansing.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Hao, M.C., Dayal, U., Hsu, M.: A Java-based visual mining infrastructure and applications. In: Proceedings of 1999 IEEE Symposium on Information Visualization, vol. 24-29, pp. 124–127 (1999)
Robinson, N., Shapcott, M.: Data mining information visualisation - beyond charts and graphs. In: Proceedings of Sixth International Conference on Information Visualisation, vol. 10- 12, pp. 577–583 (2002)
Kopanakis, I., Theodoulidis, B.: Visual data mining modeling techniques for the visualization of mining outcomes. Journal of Visual Languages & Computing 14-6, 543–589 (2003)
Manco, G., Pizzuti, C., Talia, D.: Eureka!: an interactive and visual knowledge discovery tool. Journal of Visual Languages & Computing 15-1, 1–35 (2004)
Ware, M., et al.: Interactive machine learning: letting users build classifiers. International Journal of Human-Computer Studies 55-3, 281–292 (2001)
Humphrey, M., Cunningham, S., Witten, I.: Knowledge Visualization Techniques for Machine Learning. Intelligent Data Analysis 2-1, 333–347 (1998)
Bala, J., Baik, S., Gutta, S., Hadjarian, A., Mannucci, M., Pachowicz, P.: InferView: An Integrated System for Knowledge Acquisition and Visualization. In: Proceedings of the Federal Data Mining Symposium and Exposition 1999 (1999)
See Web site at http://www.sas.com/technologies/analytics/datamining/miner/dec_trees.html
See Web site at http://kdd.ics.uci.edu/databases/census-income/census-income.data.html
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Baik, S., Bala, J., Ahn, S. (2004). Visualizing Predictive Models in Decision Tree Generation. In: Laganá, A., Gavrilova, M.L., Kumar, V., Mun, Y., Tan, C.J.K., Gervasi, O. (eds) Computational Science and Its Applications – ICCSA 2004. ICCSA 2004. Lecture Notes in Computer Science, vol 3046. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24768-5_52
Download citation
DOI: https://doi.org/10.1007/978-3-540-24768-5_52
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22060-2
Online ISBN: 978-3-540-24768-5
eBook Packages: Springer Book Archive