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Use of Correspondence Analysis in Clustering a Mixed-Scale Data Set with Missing Data

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Abstract

Correspondence analysis is a method of dimension reduction for categorical data, providing many tools that can handle complex data sets. Observations on different measurement scales can be coded to be analysed together and missing data can also be handled in the categorical framework. In this study, the method’s ability to cope with these problematic issues is illustrated, showing how a valid continuous sample space for a cluster analysis can be constructed from the complex data set from the IFCS 2017 Cluster Challenge.

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  • Michael Greenacre, 2019. "Use of Correspondence Analysis in Clustering a Mixed-Scale Data Set with Missing Data," Economics Working Papers 1626, Department of Economics and Business, Universitat Pompeu Fabra.
    • Handle: RePEc:upf:upfgen:1626
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    Cited by:

    1. Azucena Penelas-Leguía & Estela Nunez-Barriopedro & Jose María López-Sanz & Rafael Ravina-Ripoll, 2023. "Positioning analysis of Spanish politicians through their Twitter posts versus Spanish public opinion," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-11, December.
    2. Michael Greenacre & Patrick J. F Groenen & Trevor Hastie & Alfonso Iodice d’Enza & Angelos Markos & Elena Tuzhilina, 2023. "Principal component analysis," Economics Working Papers 1856, Department of Economics and Business, Universitat Pompeu Fabra.

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