Biometrical Letters Vol. 50(1), 2013, pp. 27-38
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CLUSTERING OF SYMBOLIC DATA BASED ON AFFINITY COEFFICIENT: APPLICATION TO A REAL DATA SET Áurea Sousa1, Helena Bacelar-Nicolau2, Fernando C. Nicolau3, Osvaldo Silva4 1University of Azores, Department of Mathematics, CEEAplA, and CMATI, 9501-855-Ponta Delgada, Portugal, e-mail: aurea@uac.pt 2University of Lisbon, Faculty of Psychology, Laboratory of Statistics and Data Analysis 1649-013-Lisboa, Portugal, and DataScience, e-mail: hbacelar@fp.ul.pt 3New University of Lisbon, FCT, Department of Mathematics, 2829-516-Caparica, Portugal, and DataScience, e-mail: geral@datascience.org 4University of Azores, Department of Mathematics, CMATI, 9501-855-Ponta Delgada, Portugal e-mail: osilva@uac.pt |
In this paper, we illustrate an application of Ascendant Hierarchical Cluster Analysis (AHCA) to complex data taken from the literature (interval data), based on the standardized weighted generalized affinity coefficient, by the method of Wald and Wolfowitz. The probabilistic aggregation criteria used belong to a parametric family of methods under the probabilistic approach of AHCA, named VL methodology. Finally, we compare the results achieved using our approach with those obtained by other authors.
Ascendant Hierarchical Cluster Analysis, Symbolic Data, Interval Data, Affinity Coefficient, VL Methodology
