Abstract
The opportunities of applying the Fuzzy-C-Means (FCM) algorithm for cluster analysis to study composition-property relations are discussed. Physical properties of 98 aluminosilicate glasses serve as an example. The influences of the initial partition and of the number of clusters chosen are discussed in detail. The FCM algorithm allows to find characteristic members of clusters (these form the “nuclei” of the clusters) and to find outliers of the data set under consideration. Thus the results of the cluster analysis form a basis for a deeper understanding of relations between the composition and properties of the glasses studied.
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