Systems are considered legacy when their maintenance costs raise to unmanageable levels, but they still deliver valuable benefits for companies. One intrinsic problem of this kind of system is the presence of crosscutting concerns in their architecture, hindering its comprehension and evolution. Architecture-driven modernization (ADM) is the new generation of reengineering in which models are used as main artifacts during the whole process. Using ADM, it is possible to modernize legacy systems by remodularizing their concerns in a more modular shape. In this sense, the first step is the identification of source code elements that contribute to the implementation of those concerns, a process known as concern mining. Although there exist a number of concern mining approaches in the literature, none of them are devoted to ADM, leading individual groups to create their own ad hoc proprietary solutions. In this paper, we propose an approach called crosscutting-concern knowledge discovery meta-model (CCKDM) whose goal is to mine crosscutting concerns in ADM context. Our approach employs a combination of a concern library and a K-means clustering algorithm.