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Mining Time Relaxed Gradual Moving Object Clusters

Phan Nhat Hai 1 Dino Ienco 1 Pascal Poncelet 1 Maguelonne Teisseire 1
1 TATOO - Fouille de données environnementales
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
Abstract : One of the objectives of spatio-temporal data mining is to analyze moving object datasets to exploit interesting pat- terns. Traditionally, existing methods only focus on an unchanged group of moving objects during a time period. Thus, they cannot capture object moving trends which can be very useful for better understanding the natural moving behavior in various real world applications. In this paper, we present a novel concept of "time relaxed gradual trajectory pattern", denoted real-Gpattern, which captures the object movement tendency. Additionally, we also propose an efficient algorithm, called ClusterGrowth, designed to extract the complete set of all interesting maximal real-Gpatterns. Conducted experiments on real and large synthetic datasets demonstrate the effectiveness, parameter sensitiveness and efficiency of our methods.
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Phan Nhat Hai, Dino Ienco, Pascal Poncelet, Maguelonne Teisseire. Mining Time Relaxed Gradual Moving Object Clusters. ACM SIGSPATIAL GIS, Nov 2012, Redondo Beach, United States. pp.478-481, ⟨10.1145/2424321.2424394⟩. ⟨lirmm-00798076⟩



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