TOSCA: TwO-Steps Clustering Algorithm for personal locations detection

One of the key tasks in mobility data analysis is the study of the individual mobility of users with reference to their per- sonal locations, i.e. The places or areas where they stop to perform any kind of activities. Correctly discovering such personal locations is therefore a very important problem, which is yet not very well addressed in literature. In this work we propose a robust, efficient, statistically well-founded and parameter-free personal location detection process. The algorithm, called TOSCA (TwO-Steps parameter free Clustering Algorithm), combines two clustering strategies and applies statistical tests to drive the selection of the needed parameters. The proposed solution is tested against a large set of competitors and several datasets, including synthetic and real ones. The empirical results show its ability to auto- matically adapt to different contexts yielding good accuracy and a good efficiency.