In the ever-increasing availability of massive data sets describing complex systems, i.e. systems composed of a plethora of elements interacting in a non-linear way, complex networks have emerged as powerful tools for characterizing these structures of interactions in a mathematical way. In this contribution, we explore how different Data Mining techniques can be adapted to improve such characterization. Specifically, we here describe novel techniques for optimizing network representations of different data sets; automatize the extraction of relevant topological metrics, and using such metrics toward the synthesis of high-level knowledge. The validity and usefulness of such approach is demonstrated through the analysis of medical data sets describing groups of control subjects and patients. Finally, the application of these techniques to other social and technological problems is discussed.