Moving Objects: Combining Gradual Rules and Spatial-Temporal Patterns

Abstract : Mining gradual patterns plays a crucial role in many real world applications where very large and complex numerical data must be handled, e.g., biological databases, survey databases, data streams or sensor readings. Gradual rules highlight complex order correlations of the form. Such rules have been studied for a long time and recently scalable algorithms have been proposed to addressthisissue. However, mining gradual patternsremainschallengingin mobileobject applications. In the other hand, mining frequent moving objects patterns is also very useful in many applications such as traffic management, mobile commerce, animals tracking. Those two techniques are very efficient to discover interesting rules and patterns; however, in some aspect, each individual technique could not help us to fully understand and discover interesting items and patterns. In this paper, we present a novel concept in that gradual pattern and spatio-temporal pattern are combined together to extract gradual-spatio-temporal rules. We also propose a novel algorithm, named GSTD, to extract such rules. Conducted experimentson a real dataset show that new kindsof patternscan be extracted.
Type de document :
Communication dans un congrès
ICSDM: International Conference on Spatial Data Mining and Geographical Knowledge Services, 2011, Fuzhou, China. pp.131-136, 2011
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-00798312
Contributeur : Pascal Poncelet <>
Soumis le : vendredi 8 mars 2013 - 12:29:46
Dernière modification le : mercredi 10 octobre 2018 - 14:28:11

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  • HAL Id : lirmm-00798312, version 1

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Phan Nhat Hai, Pascal Poncelet, Maguelonne Teisseire. Moving Objects: Combining Gradual Rules and Spatial-Temporal Patterns. ICSDM: International Conference on Spatial Data Mining and Geographical Knowledge Services, 2011, Fuzhou, China. pp.131-136, 2011. 〈lirmm-00798312〉

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