A Spatial-based KDD Process to Better Understand the Spatiotemporal Phenomena

Abstract : In this paper, we present a knowledge discovery process ap- plied to hydrological data. To achieve this objective, we combine succes- sive methods to extract knowledge on data collected at stations located along several rivers. Firstly, data is pre processed in order to obtain different spatial proximities. Later, we apply two algorithms to extract spatiotemporal patterns and compare them. Such elements can be used to assess spatialized indicators to assist the interpretation of ecological and rivers monitoring pressure data.
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Communication dans un congrès
CAiSE: Conference on Advanced Information Systems Engineering, Jun 2013, Valencia, Spain. CEUR-WS.org, 25th International Conference on Advanced Information Systems Engineering, 1001, 2013
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Contributeur : Hugo Alatrista Salas <>
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Dernière modification le : jeudi 11 janvier 2018 - 06:27:21
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  • HAL Id : lirmm-01090664, version 1

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Hugo Alatrista Salas, Sandra Bringay, Frédéric Flouvat, Nazha Selmaoui-Folcher, Maguelonne Teisseire. A Spatial-based KDD Process to Better Understand the Spatiotemporal Phenomena. CAiSE: Conference on Advanced Information Systems Engineering, Jun 2013, Valencia, Spain. CEUR-WS.org, 25th International Conference on Advanced Information Systems Engineering, 1001, 2013. 〈lirmm-01090664〉

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