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The paper presents the method for analysis of the learning data sets, used to create automated diagnostic modules. Graph clustering algorithm is presented and applied to the detection of the similarity between the learning examples. Possible applications of the method to the alternative fault codes labeling, ambiguity groups detection, and optimization of the existing diagnostic modules are considered. Experiments using electric machine model are presented and conclusions drawn.