Contrastive analysis for scatterplot-based representations of dimensionality reduction

Autor(es)

Marcilio-Jr, Wilson E. ; Eler, Danilo M. ^[1] ; Garcia, Rogerio E. ^[1]

Afiliação do(s) autor(es)

^[1] Marcilio-Jr, Jr., Wilson E., Sao Paulo State Univ UNESP, Fac Sci & Technol, BR-19060900 Presidente Prudente, SP - Brazil

• Tipo de documento: Artigo Científico
• Fonte: COMPUTERS & GRAPHICS-UK; v. 101, p. 46-58, DEC 2021.
• Citações Web of Science: 1

Resumo

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. (c) 2021 Elsevier Ltd. All rights reserved. (AU)

• Processo FAPESP: 18/25755-8 - Representação visual multinível para auxiliar a exploração de conjuntos de dados
• Beneficiário: Wilson Estécio Marcílio Júnior
• Modalidade de apoio: Bolsas no Brasil - Programa Capacitação - Treinamento Técnico
• Processo FAPESP: 18/17881-3 - Representação visual multinível para auxiliar a exploração de conjuntos de dados
• Beneficiário: Danilo Medeiros Eler
• Modalidade de apoio: Auxílio à Pesquisa - Regular