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Understanding Matching Data Through Their Partial Components

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Intelligent Data Engineering and Automated Learning – IDEAL 2017 (IDEAL 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10585))

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Abstract

In this paper we develop a previous work on matching data [2], inserting their contents in the more general framework of contingency tables and dealing with the dimensions problem generated by the combination of the multiple characteristics that define each row and column category. Two concepts related to the matching process are defined: propensity to match and similarity in the matching. Both measures can be divided into partial components which allow a better understanding of the underlying structure of the data. We illustrate our methodology taking as an example a labor market where each worker category and each job category is defined by the combination of two attributes: location and occupational level.

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Notes

  1. 1.

    Among the general references in the voluminous clustering literature are [9] and [4]. References about biclustering are generally more specific. [6] and [7] offer an overview about the subject.

  2. 2.

    We have not considered the propensity to match of each category of each variable on the side of the rows, with each category of other variables on the side of the columns because the analysis would be more complex and the methodological gain marginal.

References

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Author information

Authors and Affiliations

  1. Department of Industrial Organization and Business Management I, University of Seville, Seville, Spain

    Pablo Álvarez de Toledo & Fernando Núñez

  2. Department of Economics, Quantitative Methods and Economic History, Pablo de Olavide University, Seville, Spain

    Carlos Usabiaga

  3. Department of Languages and Computer Systems, University of Seville, Seville, Spain

    Antonio J. Tallón-Ballesteros

Authors
  1. Pablo Álvarez de Toledo
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  2. Fernando Núñez
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  3. Carlos Usabiaga
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  4. Antonio J. Tallón-Ballesteros
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Corresponding author

Correspondence to Antonio J. Tallón-Ballesteros .

Editor information

Editors and Affiliations

  1. University of Manchester, Manchester, United Kingdom

    Hujun Yin

  2. School of Electronic and Electrical Engineering, Nanjing University, Nanjiing, China

    Yang Gao

  3. Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Songcan Chen

  4. Guilin University of Electronic Technology, Guilin, China

    Yimin Wen

  5. Guilin University of Electronic Technology, Guilin, China

    Guoyong Cai

  6. Guilin University of Electronic Technology, Guilin, China

    Tianlong Gu

  7. Beijing University of Posts and Telecommunications, Beijing, China

    Junping Du

  8. University of Seville, Seville, Spain

    Antonio J. Tallón-Ballesteros

  9. Southeast University, Nanjing, China

    Minling Zhang

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de Toledo, P.Á., Núñez, F., Usabiaga, C., Tallón-Ballesteros, A.J. (2017). Understanding Matching Data Through Their Partial Components. In: Yin, H., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2017. IDEAL 2017. Lecture Notes in Computer Science(), vol 10585. Springer, Cham. https://doi.org/10.1007/978-3-319-68935-7_65

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  • DOI: https://doi.org/10.1007/978-3-319-68935-7_65

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-68934-0

  • Online ISBN: 978-3-319-68935-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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