  Cluster interpretation after dimensionality reduction (DR) is a ubiquitous
part of exploring multidimensional datasets. DR results are frequently
represented by scatterplots, where spatial proximity encodes similarity among
data samples. In the literature, techniques support the understanding of
scatterplots' organization by visualizing the importance of the features for
cluster definition with layout enrichment strategies. However, current
approaches usually focus on global information, hampering the analysis whenever
the focus is to understand the differences among clusters. Thus, this paper
introduces a methodology to visually explore DR results and interpret clusters'
formation based on contrastive analysis. We also introduce a bipartite graph to
visually interpret and explore the relationship between the statistical
variables employed to understand how the data features influence cluster
formation. Our approach is demonstrated through case studies, in which we
explore two document collections related to news articles and tweets about
COVID-19 symptoms. Finally, we evaluate our approach through quantitative
results to demonstrate its robustness to support multidimensional analysis.

Human-Computer Interaction;Machine Learning