<title>Visualization method and tool for interactive learning of large decision trees</title>

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.