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Mining Fuzzy Moving Object Clusters

Abstract : Recent improvements in positioning technology have led to a much wider availability of massive moving object data. One of the objectives of spatio- temporal data mining is to analyze such datasets to exploit moving objects that travel together. Naturally, the moving objects in a cluster may actually diverge temporarily and congregate at certain timestamps. Thus, there are time gaps among moving object clusters. Existing approaches either put a strong constraint (i.e. no time gap) or completely relaxed (i.e. whatever the time gaps) in dealing with the gaps may result in the loss of interesting patterns or the extraction of huge amount of extraneous patterns. Thus it is difficult for analysts to understand the object movement behavior. Motivated by this issue, we propose the concept of fuzzy swarm which softens the time gap constraint. The goal of our paper is to find all non-redundant fuzzy swarms, namely fuzzy closed swarm. As a contribution, we propose fCS-Miner algorithm which enables us to efficiently extract all the fuzzy closed swarms. Conducted experiments on real and large synthetic datasets demonstrate the ef- fectiveness, parameter sensitiveness and efficiency of our methods.
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Nhat Hai Phan, Dino Ienco, Pascal Poncelet, Maguelonne Teisseire. Mining Fuzzy Moving Object Clusters. 8th International Conference on Advanced Data Mining and Applications (ADMA), Dec 2012, Nanjing, China. pp.100-114, ⟨10.1007/978-3-642-35527-1_9⟩. ⟨lirmm-00798148⟩



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