Concentric RadViz: Visual Exploration of Multi-Task Classification

Jorge Henrique Piazentin Ono, Fabio Sikansi, Debora Cristina Correa, Fernando Vieira Paulovich, Afonso Paiva, Luis Gustavo Nonato

Research output: Chapter in Book/Conference paperConference paperpeer-review

15 Citations (Scopus)


The discovery of patterns in large data collections is a difficult task. Visualization and machine learning techniques have emerged as a way to facilitate data analysis, providing tools to uncover relevant patterns from the data. This paper presents Concentric RadViz, a general purpose class visualization system that takes into account multi-class, multi-label and multi-task classifiers. Concentric RadViz uses a force attenuation scheme, which minimizes cluttering and ambiguity in the visual layout. In addition, the user can add concentric circles to the layout in order to represent classification tasks. Our validation results and the application of Concentric RadViz for two real collections suggest that this tool can reveal important data patterns and relations. In our application, the user can interact with the visualization by selecting regions of interest according to specific criteria and changing projection parameters.

Original languageEnglish
Title of host publicationSIBGRAPI 2015: 28th Conference on Graphics, Patterns and Images
EditorsLuciano R. Oliveira, Antônio L. Apolinário Jr., Rubisley P. Lemes
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages8
ISBN (Print)978-1-4673-7962-5
Publication statusPublished - 2015
Externally publishedYes
EventSIBGRAPI 2015 - Conference on Graphics, Patterns and Images - Salvador, Brazil
Duration: 26 Aug 201529 Aug 2015


ConferenceSIBGRAPI 2015 - Conference on Graphics, Patterns and Images
Internet address


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  • Honorable Mention (paper)

    Piazentin, Jorge (Recipient), Sikansi, Fabio (Recipient), Correa, Debora (Recipient), Paulovich, Fernando (Recipient) & Paiva, Afonso (Recipient), 2015

    Prize: Award

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