A visualization tool for interactive learning of large decision trees

Decision tree induction is certainly among the most applicable learning techniques due to its power and simplicity. Howevel; learning decision trees from large datasets, particularly in data mining, is quite different from learning from small or moderately sized datasets. 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 an visualization tool for interactive learning of large decision trees, that includes a new visualization technique called l 2 S D (stands for Trees 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 ( I ) how to visualize efficiently large decision trees; and (2 ) how to visualize decision trees in the learning process.