Contrastive analysis for scatterplot-based representations of dimensionality reduction

TL;DR

This paper presents a contrastive analysis methodology for scatterplot-based dimensionality reduction, enabling detailed cluster interpretation and feature influence exploration, especially focusing on differences among clusters.

Contribution

It introduces a novel contrastive analysis approach and bipartite graph visualization for better understanding cluster formation in DR scatterplots, addressing limitations of global-focused methods.

Findings

Effective in exploring cluster differences in case studies

Robust quantitative evaluation supports multidimensional analysis

Enhances interpretability of feature influence on clusters

Abstract

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.