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Cluster interpretation after dimensionality reduction (DR) is a ubiquitouspart of exploring multidimensional datasets. DR results are frequentlyrepresented by scatterplots, where spatial proximity encodes similarity amongdata samples. In the literature, techniques support the understanding ofscatterplots' organization by visualizing the importance of the features forcluster definition with layout enrichment strategies. However, currentapproaches usually focus on global information, hampering the analysis wheneverthe focus is to understand the differences among clusters. Thus, this paperintroduces a methodology to visually explore DR results and interpret clusters'formation based on contrastive analysis. We also introduce a bipartite graph tovisually interpret and explore the relationship between the statisticalvariables employed to understand how the data features influence clusterformation. Our approach is demonstrated through case studies, in which weexplore two document collections related to news articles and tweets aboutCOVID-19 symptoms. Finally, we evaluate our approach through quantitativeresults to demonstrate its robustness to support multidimensional analysis.

Contrastive analysis for scatterplot-based representations of dimensionality reduction