Interactive Exploration of Decision Tree Results

Our investigation aims at interactively exploring the decision tree results obtained by the machine-learning algorithms like C4.5. We propose an interactive graphical environment using the new radial tree layout, zoom/pan techniques and some existing visualization methods like explorer-like, hierarchical visualization, interactive techniques to represent large decision trees in a graphical mode more intuitive than the results in output of usual decision tree algorithms. The interactive exploration system on one hand can preserve the global view in a large representation of radial layout, zoom/pan techniques and on the other hand, it also provides a very good performance for an interesting sub-tree in the explorer-like view with simplicity, speed of task completion, ease of use and user understanding. The user can easily extract inductive rules and prune the tree in the post-processing stage. He has a better understanding of the obtained decision tree models. The numerical test results with real datasets show that the proposed methods have given an insight into decision tree results.