A spatial-based KDD process to better understand the spatiotemporal phenomena

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In this paper, we present a knowledge discovery process applied to hydrological data. To achieve this objective, we combine successive methods to extract knowledge on data collected at stations located along several rivers. Firstly, data is pre processed in order to obtain diffierent 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.

Idioma originalInglés
PublicaciónCEUR Workshop Proceedings
EstadoPublicada - 2013
Publicado de forma externa
Evento2013 Doctoral Consortium of the 25th International Conference on Advanced Information Systems Engineering, CAiSE 2013 - Valencia, Espana
Duración: 21 jun. 201321 jun. 2013


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