A class-based evaluation approach to assess multidimensional projections

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Teixeira, Jaqueline ; Marcilio-Jr, Wilson E. ; Eler, Danilo M. ; Artero, Almir ; Brandoli, Bruno ; Banissi, E ; Khosrow-Shahi, F ; Ursyn, A ; Bannatyne, MWM ; Pires, JM ; Datia, N ; Nazemi, K ; Kovalerchuk, B ; Counsell, J ; Agapiou, A ; Vrcelj, Z ; Chau, HW ; Li, MB ; Nagy, G ; Laing, R ; Francese, R ; Sarfraz, M ; Bouali, F ; Venturini, G ; Trutschl, M ; Cvek, U ; Muller, H ; Nakayama, M ; Temperini, M ; DiMascio, T ; Sciarrone, F ; Rossano, V ; Dorner, R ; Caruccio, L ; Vitiello, A ; Huang, WD ; Risi, M ; Erra, U ; Andonie, R ; Ahmad, MA ; Figueiras, A ; Cuzzocrea, A ; Mabakane, MS

• Tipo de documento: Artigo Científico
• Fonte: 2020 24TH INTERNATIONAL CONFERENCE INFORMATION VISUALISATION (IV 2020); v. N/A, p. 8-pg., 2020-01-01.

Resumo

Multidimensional projection techniques have been widely used to visually explore datasets due to their ability to generate representations that preserve similarity relations of data points into lower dimensional spaces. To evaluate if the embedded space reflects high-dimensional structures, measures are usually employed to return a quality score of the whole projection. In contrast to this idea, we evaluate the embedded layouts by assessing each class of the datasets at a time by using well-known quality measures. In addition, we propose assessing multidimensional projection techniques using ROC curves. Experimental results on two datasets show that our approach can be useful to discover how classes interact each other by using different visualization techniques and how close-related they are without thoroughly exploring the layouts. ROC curves proved to be a good measure for analyzing projection techniques and can give highly valuable feedback to users when exploring multidimensional data. (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