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Proceedings Paper

Visualization method and tool for interactive learning of large decision trees
Author(s): Trong Dung Nguyen; TuBao Ho
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Paper Abstract

When learning from large datasets, decision tree induction programs often produce very large trees. How to visualize efficiently trees in the learning process, particularly large trees, is still questionable and currently requires efficient tools. This paper presents a visualization method and tool for interactive learning of large decision trees, that includes a new visualization technique called T2.5D (stands for Tress 2.5 Dimensions). After a brief discussion on requirements for tree visualizers and related work, the paper focuses on presenting developing techniques for the issues (1) how to visualize efficiently large decision trees; and (2) how to visualize decision trees in the learning process.

Paper Details

Date Published: 12 March 2002
PDF: 9 pages
Proc. SPIE 4730, Data Mining and Knowledge Discovery: Theory, Tools, and Technology IV, (12 March 2002); doi: 10.1117/12.460208
Show Author Affiliations
Trong Dung Nguyen, Japan Advanced Institute of Science and Technology (Japan)
TuBao Ho, Japan Advanced Institute of Science and Technology (Japan)


Published in SPIE Proceedings Vol. 4730:
Data Mining and Knowledge Discovery: Theory, Tools, and Technology IV
Belur V. Dasarathy, Editor(s)

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