Contextual location prediction using spatio-temporal clustering

Purpose - The prediction of a context, especially of a user's location, is a fundamental task in the field of pervasive computing. Such predictions open up a new and rich field of proactive adaptation for context-aware applications. This study/paper aims to propose a methodology that predicts a user's location on the basis of a user's mobility history. Design/methodology/approach - Contextual information is used to find the points of interest that a user visits frequently and to determine the sequence of these visits with the aid of spatial clustering, temporal segmentation and speed filtering. Findings - The proposed method was tested with a real data set using several supervised classification algorithms, which yielded very interesting results. Originality/value - The method uses contextual information (current position, day of the week, time and speed) that can be acquired easily and accurately with the help of common sensors such as GPS.