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

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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.
Original languageEnglish
Pages (from-to)101-112
Number of pages12
Publication statusPublished - 7 Apr 2023


  • Decision trees
  • Interpretability
  • Nonlinear dimensionality reduction
  • Visualization
  • t-SNE


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