  Exploring multidimensional datasets is a ubiquitous part of the ones working
with data, where interpreting clusters is one of the main tasks. These
multidimensional datasets are usually encoded using scatter-plots
representations, where spatial proximity encodes similarity among data samples.
In the literature, techniques try to understand the scatter plot organization
by visualizing the importance of the features for clusters definition with
interaction and layout enrichment strategies. However, the approaches used to
interpret dimensionality reduction usually do not differentiate clusters well,
which hampers analysis where the focus is to understand the differences among
clusters. This paper introduces a methodology to visually explore
multidimensional datasets and interpret clusters' formation based on the
contrastive analysis. We also introduce a bipartite graph to visually interpret
and explore the relationship between the statistical variables used to
understand how the attributes influenced cluster formation. Our methodology is
validated through case studies. We explore a multivariate dataset of patients
with vertebral problems and two document collections, one related to news
articles and other related to tweets about COVID-19 symptoms. Finally, we also
validate our approach through quantitative results to demonstrate how it can be
robust enough to support multidimensional analysis.
