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Visual Data Mining: A Comparative Analysis of Selected Datasets

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Intelligent Systems Design and Applications (ISDA 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1351))

Abstract

This paper presents data preprocessing operations and visualisation techniques, carried out on the following datasets: Teaching Assistant Evaluation dataset, Statlog (Australian Credit Approval) dataset, Letter Recognition, Connectionist Bench (Sonar, Mines vs. Rocks) dataset, and Poker Hand dataset. These datasets are from the University of California Irvine (UCI) Machine Learning Repository. Further, appropriate visualisation techniques are applied to the five selected datasets depending on the properties that are supported by the visualisation techniques used. In the end, this paper offers a template for researchers, data scientists, and other data users, in selecting the right preprocessing operations and appropriate visualisation techniques when using these datasets.

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Author information

Authors and Affiliations

  1. Department of Computer Science, Eastern Michigan University (EMU), Ypsilanti, MI, USA

    Ujunwa Mgboh

  2. Department of Computer Science, University of Nigeria, Nsukka, Nigeria

    Blessing Ogbuokiri

  3. School Computer Science and Applied Mathematics, University of the Witwatersrand, Johannesburg, South Africa

    George Obaido

  4. Department of Information Technology, Walter Sisulu University, Johannesburg, South Africa

    Kehinde Aruleba

Authors
  1. Ujunwa Mgboh
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  2. Blessing Ogbuokiri
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  3. George Obaido
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  4. Kehinde Aruleba
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Corresponding author

Correspondence to Blessing Ogbuokiri .

Editor information

Editors and Affiliations

  1. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR Labs), Auburn, WA, USA

    Ajith Abraham

  2. Department of Computer Science, Università degli Studi di Milano, Milan, Milano, Italy

    Vincenzo Piuri

  3. Machine Intelligence Research Labs (MIR Labs), Auburn, WA, USA

    Niketa Gandhi

  4. Campus Centre de Créteil, Université Paris-Est Créteil, Créteil, France

    Patrick Siarry

  5. Department of Construction Management and Real Estate, Vilnius Gediminas Technical University, Vilnius, Lithuania

    Arturas Kaklauskas

  6. School of Engineering, Instituto Superior de Engenharia do Porto, Porto, Portugal

    Ana Madureira

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Mgboh, U., Ogbuokiri, B., Obaido, G., Aruleba, K. (2021). Visual Data Mining: A Comparative Analysis of Selected Datasets. In: Abraham, A., Piuri, V., Gandhi, N., Siarry, P., Kaklauskas, A., Madureira, A. (eds) Intelligent Systems Design and Applications. ISDA 2020. Advances in Intelligent Systems and Computing, vol 1351. Springer, Cham. https://doi.org/10.1007/978-3-030-71187-0_35

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