High-dimensional data with many features are usually challenging to represent with standard visualization techniques. Usually, one has to resort to dimensionality reduction techniques such as PCA, MDS or -SNE to represent such data. Such dimensionality reduction techniques make it possible to highlight the high-dimensional structures of data. In many of such visualizations, comparable instances appear to form visual clusters. However, no feedback is directly given by these techniques to the user about the features that make the instances cluster together in the visualization. As such, the interpretation of which features define a given visual cluster is a complicated task. In this paper, we propose a novel interactive approach (called Interactive eXplanation of Visual Clusters — IXVC) to explain dimensionality reduction visualizations by mapping their clusters to explanations provided by decision trees. The decision trees use features in high-dimensional data to explain two-dimensional clusters, filling the gap between the dimensionality reduction visualization and the original data.