Skip to Main Content
Data uncertainty is often involved in moving object tracking in mobile computing environment due to reasons such as imprecise measurement or sampling errors. Data mining of such positions of moving objects attracts more and more research interest recently. The definitions of probabilistic core object and probabilistic density-reachability are presented and a probabilistic clustering algorithm for location data of moving objects is proposed, based on DBSCAN algorithm and probabilistic index on moving objects. Experiment results show that the proposed algorithm outperforms other clustering algorithm we knew for moving objects in update rate needed and efficiency of clustering.