Clustering Moving Objects Based On A Moving Clustering Feature Tree

Clustering moving objects is a challenging task, especially when space consumption must be flexibly and efficiently adjusted for adapting to dynamic object movements. In this paper we develop an efficient approach for managing moving objects and predicting the essential time when moving clusters may need to be updated. Under our approach, moving objects are first inserted into a moving clustering feature (MCF) tree such that similar moving objects are grouped into moving micro clusters (MMCs). Each MMC is represented by a vector that summarizes the position and velocity information of its member objects. Based on this summarized information, a set of simple formulas is developed to efficiently predict when the contents of MMCs must be changed. High quality final clusters can then be obtained by executing a global clustering algorithm against MMCs. In addition, our approach can efficiently condense MMCs or the MCF tree to conserve space. We will also show that our approach can easily accommodate velocity changes by objects. Finally, we study the performance and quality of our approach.