Exploiting multiple radii to learn significant locations

Location contexts are important for many context-aware applications. A significant location is a specialized form of location context for expressing a user's daily activity. We propose a method to cluster positions measured by cellular phones into significant locations with multiple radii. Cellular phones we used are equipped with a positioning system, where data can be taken in low frequency with wide-varying estimated errors. In order to learn significant locations, our system exploits multiple radii for coping with these characteristics and for adapting to a variety of users' spatial behavioral patterns. We also discuss appropriate parameters for our clustering method.