Data visualization using decision trees and clustering

Decision trees are simple and powerful tools for knowledge extraction and visual analysis. However, when applied to complex datasets available nowadays, they tend to be large and uneasy to visualize. This difficulty can be overcome by clustering the dataset and representing the decision tree of each cluster independently. In order to apply the clustering more efficiently, we propose a method for adapting clustering results with a view to simplifying the decision tree obtained from each cluster. A prototype has been implemented, and the benefits of the proposed method are shown using the results of several experiments performed on the UCI benchmark datasets.