Decision Tree Visualization for High-Dimensional Numerical Data

Big Data enforces the usage of data mining techniques to provide the user valuable insights. There is a broad range of data mining and machine learning techniques tackling different tasks. Generic approaches are classification algorithms, which label given data points by a pretrained model. Decision tree-based classification algorithms are often used, as they provide a human-explainable model, which can be represented by simple induced rules. In order to present the classification results and the concrete model to the user, there exist for both problems a set of different solutions. Current visualizations either project labeled data into the plane or three-dimensional space, or the visualizations illustrate the decision tree rules as e.g. graph structures. But they lack to provide a possibility to show both, data and the model, within a single plot. Therefore, we propose a projection strategy to present both decision tree model and data in a single plot. Furthermore, we developed an interactive visualization to showcase the proposed approach and evaluated the visualization on open-source datasets. The results show that the plots can be computed in short time and projection adjustments are reasonably low.