The authors present an example of the application of cluster analysis in commodity science. The aim of the analysis was to find, on the ground of chemical composition, groups of mineral and spring waters in which the objects of the same group exhibited the highest possible degree of similarity, while objects of different groups exhibited the lowest. Euclidean distance method was used to calculate the distance between the objects. The agglomerations were created by using the nearest neighbour algorithm.
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