Density-based probabilistic clustering of uncertain moving objects

In the environment with objects moving randomly, the positions of moving objects can be modeled as a range of possible values, associated with a probability density function. Data mining of such positions of uncertain moving objects attracts more and more research interest recently. The definitions of probabilistic core object and probabilistic density-reachability are presented and a density-based probabilistic clustering algorithm for uncertain moving objects is proposed, based on DBSCAN algorithm and probabilistic index on uncertain moving objects. Simulation results show that the proposed algorithm outperforms other density-based clustering algorithm for uncertain moving objects in accuracy and update rate needed for clustering.