Abstract
During electorate campaigns, linguistic appraisals of presidental candidates given by voters are essential. In order to deal with linguistic appraisals and cluster analysis, this paper presents the results of grouping the United States 2016 presidential candidates using linguistic appraisals collected from a political survey. To do this, we have developed an agglomerative hierarchical clustering procedure based on consensus through the concept of ordinal proximity measure.
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Notes
- 1.
The computations for obtaining the corresponding sequential consensus and similarity vectors have been performed with MATLAB.
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Acknowledgments
The authors are grateful to Victoriano Ramírez for the information about the data source used in this paper. The financial support from the Spanish Ministerio de Economía y Competitividad (project ECO2016-77900-P) and ERDF are acknowledged.
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González del Pozo, R., García-Lapresta, J.L., Pérez-Román, D. (2018). Clustering U.S. 2016 Presidential Candidates Through Linguistic Appraisals. In: Kacprzyk, J., Szmidt, E., Zadrożny, S., Atanassov, K., Krawczak, M. (eds) Advances in Fuzzy Logic and Technology 2017. EUSFLAT IWIFSGN 2017 2017. Advances in Intelligent Systems and Computing, vol 642. Springer, Cham. https://doi.org/10.1007/978-3-319-66824-6_13
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DOI: https://doi.org/10.1007/978-3-319-66824-6_13
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