Analog circuits are widely used in different fields such as medicine, military, aviation and are critical for the development of reliable electronic systems. Testing and diagnosis are important tasks which detect and localize defects in the circuit under test as well as improve quality of final product. Output responses of fault-free and faulty behavior of analog circuit can be represented by infinite set of values due to tolerances of internal components. The data mining methods may improve quality of fault diagnosis in the case of big data processing. The technique of aggregation the classes of fault diagnostic responses, based on association rule mining, is proposed. The technique corresponds to the simulation before test concept: a fault dictionary is generated by collecting output signals are preprocessed by wavelet transformation for fault-free and faulty conditions. Calssificator is based on k-nearest neighbor (k-NN) and association rule mining algorithm. The fault diagnostic technique trained and tested using data obtained reliable results from real sample circuits achieve more than 99,09% accuracy in classifying faulty conditions and more than 99,08% of coverage. The proposed technique is completely automated and can be extended.