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.
@article{bwmeta1.element.doi-10_2478_bile-2013-0015,
author = {\'Aurea Sousa and Helena Bacelar-Nicolau and Fernando C. Nicolau and Osvaldo Silva},
title = {Clustering of Symbolic Data based on Affinity Coefficient: Application to a Real Data Set},
journal = {Biometrical Letters},
volume = {50},
year = {2013},
pages = {27-38},
zbl = {1286.62060},
language = {en},
url = {http://dml.mathdoc.fr/item/bwmeta1.element.doi-10_2478_bile-2013-0015}
}
Áurea Sousa; Helena Bacelar-Nicolau; Fernando C. Nicolau; Osvaldo Silva. Clustering of Symbolic Data based on Affinity Coefficient: Application to a Real Data Set. Biometrical Letters, Tome 50 (2013) pp. 27-38. http://gdmltest.u-ga.fr/item/bwmeta1.element.doi-10_2478_bile-2013-0015/
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