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VizARE: An Intermediate Representation to Support the Visualization of Association Rules in Data Mining

Author

Listed:
  • Carlos Fernandez-Basso

    (Department Computer Science and AI, CITIC-UGR, University of Granada, Periodista Daniel Saucedo Aranda, 18015 Granada, Spain)

  • Maria Dolores Ruiz

    (Department Computer Science and AI, CITIC-UGR, University of Granada, Periodista Daniel Saucedo Aranda, 18015 Granada, Spain)

  • Miguel Molina-Solana

    (Department Computer Science and AI, CITIC-UGR, University of Granada, Periodista Daniel Saucedo Aranda, 18015 Granada, Spain
    Data Science Institute, Imperial College London, London SW7 2AZ, UK)

  • Maria J. Martin-Bautista

    (Department Computer Science and AI, CITIC-UGR, University of Granada, Periodista Daniel Saucedo Aranda, 18015 Granada, Spain)

Abstract

Data mining techniques are currently highly useful and widely used in industry, business and government. However, their broad adoption is sometimes limited because non-expert users are required to accurately interpret and deal with the complex results obtained. In this paper, we put forward a methodology for the display of association rules using an intermediate form. This technique enables efficient processing of the rules by generating a standard format through a graph structure that allows us to adapt the rules to different display tools. We also show some illustrative examples of the usefulness of this intermediate form.

Suggested Citation

  • Carlos Fernandez-Basso & Maria Dolores Ruiz & Miguel Molina-Solana & Maria J. Martin-Bautista, 2026. "VizARE: An Intermediate Representation to Support the Visualization of Association Rules in Data Mining," Future Internet, MDPI, vol. 18(7), pages 1-30, July.
    • Handle: RePEc:gam:jftint:v:18:y:2026:i:7:p:374-:d:1993768
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