The discovery of a person's personally important places involves obtaining the physical locations for a person's places that matter to his daily life, and routines. This problem is driven by the requirements from emerging location-aware applications, which allow a user to pose queries and obtain information in reference to places, e.g., "home", "work"' or "Northwest Health Club". It is a challenge, to map from physical locations to personally meaningful places because GPS tracks are continuous data both spatially and temporally, while most existing data mining techniques expect discrete data. Previous work has explored algorithms to discover personal places from location data. However, they all have limitations. Our work proposes a two-step approach that discretized continuous GPS data into places and learns important places from the place features. Our approach was validated using real user data and shown to have good accuracy when applied in predicting not only important and frequent places, but also important and not so frequent places.