Smart card data gathered by Automated Fare Collection (AFC) systems are a valuable resource for studying urban mobility. In this paper, we propose two approaches to clustering smart card data that can be used to extract mobility patterns in a public transportation system. Two complementary standpoints are considered: a station-oriented, operational point of view and a passenger-focused one. The first approach clusters stations based on when their activity occurs, i.e. how trips made at the stations are distributed over time. The second approach makes it possible to identify groups of passengers that have similar boarding times aggregated into weekly profiles. By applying our approaches to a real dataset issued from the metropolitan area of Rennes (France) we illustrate how they can help reveal valuable insights about urban mobility like the presence of different station key-roles such as residential stations used mostly in the mornings, work stations used only in the evening and almost exclusively during weekdays, etc. as well as different passenger behaviors ranging from the sporadic and diffuse usage to typical commute practices. By cross-comparing passenger clusters with fare types, we also highlight how certain usages are more specific to particular types of passengers.