Using Clustering to Improve Decision Trees Visualization

Decision trees are simple and powerful decision support tools, and their graphical nature can be very useful for visual analysis tasks. However, decision trees tend to be large and hard to display when they are built from complex real world data. This paper proposes an original solution to optimize the visual representation of decision trees obtained from data. The solution combines clustering and feature construction, and introduces a new clustering algorithm that takes into account the visual properties and the accuracy of decision trees. A prototype has been implemented, and the benefits of the proposed method are shown using the results of several experiments performed on the UCI datasets.