K-Medoids clustering is once again applied to the resulting training technology distance matrices, from which two medoid technologies are identified for each patent indicator subset, in each training condition. At this point the test technologies can now be evaluated individually against the two medoid curves identified in each training condition, in order to determine the closest medoid to the current test technology. This provides a classification for the test technologies based on each training condition and each patent indicator subset. From this the number of test technologies misclassified based on the current training condition can be determined. This in turn is then used to calculate the average number of test technologies misclassified for each patent indicator grouping across all of the training conditions considered. Finally, the results are sorted in terms of the minimum average number of misclassifications in order to rank the robustness of each patent indicator grouping. This procedure is illustrated in Fig. \ref{428246}.