This paper focuses on mining human strategies by observing their actions. Our application domain is an HCI study aimed at discovering general strategies used by software users and understanding how such strategies relate to gender and success. We cast this as a sequential pattern discovery problem, where user strategies are manifested as sequential patterns. Problematically, we found that the patterns discovered by standard algorithms were difficult to interpret and provided limited information about high-level strategies. To help interpret the patterns and extract general strategies, we examined multiple ways of clustering the patterns into meaningful groups, which collectively led to interesting findings about user behavior both in terms of gender differences and problem-solving success. As a real-world application of data mining techniques, our work led to the discovery of new strategic patterns that are linked to user success and had not been revealed in more than nine years of manual empirical work. As a case study, our work highlights important research directions for making data mining more accessible to non-experts.