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Discretizing Numerical Attributes: An Analysis of Human Perceptions

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New Trends in Database and Information Systems (ADBIS 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1652))

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Abstract

To partition numerical attributes, machine learning (ML) has used a variety of discretization approaches that partition the numerical attribute into intervals. However, an effective method for discretization is still missing in various ML approaches, e.g., association rule mining. Moreover, the existing discretization techniques do not reflect best the impact of the independent numerical factor on the dependent numerical target factor. The main objective of this research is to develop a benchmark approach for partitioning numerical factors. We present an in-depth analysis of human perceptions of partitioning a numerical factor and compare it with one of our proposed measures. We also examine the perceptions of various experts in data science, statistics and engineering disciplines by using a series of graphs with numerical data. The analysis of the collected responses indicates that \(68.7\%\) of the human responses were approximately close to the values obtained by the proposed method. Based on this analysis, the proposed method may be used as one of the methods for discretizing the numerical attributes.

This work has been partially conducted in the project “ICT programme” which was supported by the European Union through the European Social Fund.

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Notes

  1. 1.

    https://github.com/minakshikaushik/LSQM-measure.git.

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Authors and Affiliations

  1. Information Systems Group, Tallinn University of Technology, Akadeemia tee 15a, 12618, Tallinn, Estonia

    Minakshi Kaushik, Rahul Sharma & Dirk Draheim

  2. Jaypee Institute of Information Technology, Noida, India

    Ankit Vidyarthi

Authors
  1. Minakshi Kaushik
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  2. Rahul Sharma
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  3. Ankit Vidyarthi
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  4. Dirk Draheim
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Corresponding author

Correspondence to Minakshi Kaushik .

Editor information

Editors and Affiliations

  1. Politecnico di Torino, Turin, Italy

    Silvia Chiusano

  2. Politecnico di Torino, Turin, Italy

    Tania Cerquitelli

  3. Poznań University of Technology, Poznań, Poland

    Robert Wrembel

  4. Norwegian University of Science and Technology, Trondheim, Norway

    Kjetil Nørvåg

  5. University of Genoa, Genoa, Italy

    Barbara Catania

  6. CNRS, Villeurbanne Cedex, France

    Genoveva Vargas-Solar

  7. University of Calabria, Rende, Italy

    Ester Zumpano

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Kaushik, M., Sharma, R., Vidyarthi, A., Draheim, D. (2022). Discretizing Numerical Attributes: An Analysis of Human Perceptions. In: Chiusano, S., et al. New Trends in Database and Information Systems. ADBIS 2022. Communications in Computer and Information Science, vol 1652. Springer, Cham. https://doi.org/10.1007/978-3-031-15743-1_18

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  • DOI: https://doi.org/10.1007/978-3-031-15743-1_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-15742-4

  • Online ISBN: 978-3-031-15743-1

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