Knowledge discovery and data mining is a complex, multifaceted task that requires the integration of various approaches, including machine learning, statistics, and data processing algorithms. The main challenge is the need to extract useful information from large, often unstructured and noisy data, and to interpret the results to make effective decisions. The proposed method is based on the morphological approach framework, which involves the use of morphological analysis to extract useful information from structured Big Data. The morphological approach focuses on analyzing and interpreting morphological structures of systems to extract the required information. The considered approach is based on systems theory, set theory and cluster analysis. A similarity measure is introduced to evaluate the correct partitioning of a morphological set. The use of big data identifies patterns, trends and relationships between attributes of systems. Through the use of Big Data, morphological analysis can be more accurate and efficient, advancing fields such as knowledge discovery and data mining. Solving these problems opens up great opportunities for the use of data in all areas of human endeavor. The proposed approach has been used in applied engineering fields such as Aerospace, IT, technological innovation.