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Consideration of Detecting Data and Functional Dependency in Tabular Data with Missing Values by the Obtained Rules

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Rough Sets (IJCRS 2024)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14839))

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Abstract

Functional dependency is an essential concept between attributes in relational algebra and tabular data analysis, and it is applied to the decomposition of tabular data. On the other hand, data dependency is a concept between attribute values. Functional dependency is usually given, and data dependency is often recognized after data analysis. We may recognize the hidden functional dependency as a particular case of data dependency. In this paper, we apply the rules obtained by the NIS-Apriori-based rule generator, which was implemented to handle rules from tabular and tabular data with missing values. We detect some candidates CONs of condition attributes that affect the decision attribute Dec using the obtained rules. We then apply the same rule generator specifying the detected one CON to determine the actual degree of dependency. This step eliminates the need to enumerate all CONs to understand dependencies and can handle extended dependencies for DIS and NIS. A running example using the implemented tools is also provided.

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Acknowledgments

The authors thank the reviewers for their helpful comments. Part of this work is supported by JSPS (Japan Society for the Promotion of Science) KAKENHI Grant Number JP20K11954.

Author information

Authors and Affiliations

  1. Department of Data Science, Shimonoseki City University, 2-1-1 Daigakucho, Shimonoseki, 751-8510, Japan

    Hiroshi Sakai & Junzo Watada

  2. Kyushu Institute of Technology, Kitakyushu, Japan

    Hiroshi Sakai

  3. Tokyo University of Information Sciences, 4-1 Onaridai, Wakaba-ku, Chiba, 265-8501, Japan

    Michinori Nakata

  4. Institute of Informatics, University of Warsaw, Warsaw, Mazowieckie, Poland

    Dominik Ślęzak

  5. Waseda University, Kitakyushu, Japan

    Junzo Watada

Authors
  1. Hiroshi Sakai
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  2. Michinori Nakata
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  3. Dominik Ślęzak
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  4. Junzo Watada
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Corresponding author

Correspondence to Hiroshi Sakai .

Editor information

Editors and Affiliations

  1. Saint Mary's University, Halifax, NS, Canada

    Mengjun Hu

  2. Ghent University, Ghent, Belgium

    Chris Cornelis

  3. California State University, San Bernardino, CA, USA

    Yan Zhang

  4. Saint Mary's University, Halifax, NS, Canada

    Pawan Lingras

  5. University of Warsaw, Warsaw, Poland

    Dominik Ślęzak

  6. University of Regina, Regina, SK, Canada

    JingTao Yao

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Sakai, H., Nakata, M., Ślęzak, D., Watada, J. (2024). Consideration of Detecting Data and Functional Dependency in Tabular Data with Missing Values by the Obtained Rules. In: Hu, M., Cornelis, C., Zhang, Y., Lingras, P., Ślęzak, D., Yao, J. (eds) Rough Sets. IJCRS 2024. Lecture Notes in Computer Science(), vol 14839. Springer, Cham. https://doi.org/10.1007/978-3-031-65665-1_8

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  • DOI: https://doi.org/10.1007/978-3-031-65665-1_8

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  • Online ISBN: 978-3-031-65665-1

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