document: Clustering moving objects Due to the advances in positioning technologies , the real time information of moving objects becomes increasingly available , which has posed new challenges to the database research . As a long-standing technique to identify overall distribution patterns in data , clustering has achieved brilliant successes in analyzing static datasets . In this paper , we study the problem of clustering moving objects , which could catch interesting pattern changes during the motion process and provide better insight into the essence of the mobile data points . In order to catch the spatial-temporal regularities of moving objects and handle large amounts of data , micro-clustering ( 20 ) is employed . Efficient techniques are proposed to keep the moving micro-clusters geographically small . Important events such as the collisions among moving micro-clusters are also identified . In this way , high quality moving micro-clusters are dynamically maintained , which leads to fast and competitive clustering result at any given time instance . We validate our approaches with a through experimental evaluation , where orders of magnitude improvement on running time is observed over normal K-Means clustering method ( 14 ) .
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