Abstract
In this paper, we consider a cluster analysis for complete rankings of N items that aims to identify typical groups of rank choices. The “K-means” procedure based on Lee distance is studied in details and several asymptotical results for large values of N are derived. An algorithm for approximating the normalizing constant in the clustering procedure is proposed by using some properties of Lee distance. In order to compare the clustering method based on Lee distance to those based on other distances on permutations, we apply the presented procedure to a data set obtained from the results of the American Psychological Association presidential election.
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Acknowledgements
The work of N.N. was supported by the National Science Fund of Bulgaria under Grant KP-06-N32/8. The work of E.S. has been partially supported by Grant No BG05M2OP001-1.001-0003, financed by the Science and Education for Smart Growth Operational Program (2014-2020) and co-financed by the European Union through the European structural and Investment funds.
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Nikolov, N.I., Stoimenova, E. (2021). Rank Data Clustering Based on Lee Distance. In: Georgiev, I., Kostadinov, H., Lilkova, E. (eds) Advanced Computing in Industrial Mathematics. BGSIAM 2018. Studies in Computational Intelligence, vol 961. Springer, Cham. https://doi.org/10.1007/978-3-030-71616-5_27
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DOI: https://doi.org/10.1007/978-3-030-71616-5_27
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