文件名称:Road-Network-Aware-Trajectory-Clustering
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Mining trajectory data has been gaining significant interest in recent years. However, existing approaches to trajectory
clustering are mainly based on density and Euclidean distance measures. We argue that when the utility of spatial clustering of
mobile object trajectories is targeted at road-network aware location-based applications, density and Euclidean distance are no
longer the effective measures. This is because traffic flows in a road network and the flow-based density characterization become
important factors for finding interesting trajectory clusters.
clustering are mainly based on density and Euclidean distance measures. We argue that when the utility of spatial clustering of
mobile object trajectories is targeted at road-network aware location-based applications, density and Euclidean distance are no
longer the effective measures. This is because traffic flows in a road network and the flow-based density characterization become
important factors for finding interesting trajectory clusters.
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Road Network Aware Trajectory Clustering.pdf