DT-SNE: t-SNE discrete visualizations as decision tree structures

Visualizations are powerful tools that are commonly used by data scientists to get more insights about their high dimensional data. One can for example cite t-SNE, which is probably one of the most famous and widely-used visualization techniques. However, t-SNE is a nonlinear and non-parametric technique that makes it suffer from a lack of interpretability. In this paper, we present a new technique inspired by t-SNE’s objective function that combines its ability to build nice visualizations with the interpretability of decision trees. This new visualization technique, called DT-SNE, can be seen as a discrete visualization technique where groups of instances are provided, as well as a ranking between them. The decision rules of the decision tree provide clear insights to interpret these different groups.