The goal of this work is to identify markers for the onset of emotional issues, such as depression and anxiety, in high-school-age children. Such markers may involve declining academic performance, truancy, and behavioural issues in schools. The ability to identify markers predicting the onset of emotional issues in school-age children would allow schoolteachers and administrators to notify parents and guardians of the children affected as soon as the markers are detected. This would in turn facilitate early diagnosis and treatment for children who are indeed developing emotional issues. To accomplish our goal, we applied unsupervised learning methods to partition the input dataset into clusters with similar characteristics with respect to potential markers, such as declining or fluctuating academic performance.