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Based on Natural phenomena many creatures form large social groups and move in regular patterns. Traditional works focus on finding the movement patterns of each single object or all objects. This paper propose an efficient distributed mining algorithm to jointly identify a group of moving objects and discover their movement patterns in Wireless Sensor Networks (WSN). The algorithm consists of a local mining phase and a cluster ensembling phase. The local mining phase adopts the Variable Length Markov (VMM) model together with Probabilistic Suffix Tree (PST) to find the moving patterns, as well as Highly Connected Component (HCC) to partition the moving objects.
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