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.

降维后的聚类解释 (DR) 是探索多维数据集的一个普遍部分。DR 结果通常由散点图表示，其中空间邻近度对数据样本之间的相似性进行编码。在文献中，技术通过使用布局丰富策略可视化集群定义的特征的重要性来支持对散点图组织的理解。然而，当前的方法通常侧重于全局信息，当重点是了解集群之间的差异时，就会妨碍分析。因此，本文介绍了一种基于对比分析的可视化探索 DR 结果和解释集群形成的方法。我们还引入了一个二部图来直观地解释和探索用于理解数据特征如何影响集群形成的统计变量之间的关系。我们的方法通过案例研究得到证明，在案例研究中，我们探索了与关于 COVID-19 症状的新闻文章和推文相关的两个文档集。最后，我们通过定量结果评估我们的方法，以证明其支持多维分析的稳健性。