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

Hugo Alatrista-Salas

Producción científica: Contribución a una conferencia


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
EstadoPublicada - 1 ene. 2013
Publicado de forma externa
EventoCEUR Workshop Proceedings -
Duración: 1 ene. 2017 → …


ConferenciaCEUR Workshop Proceedings
Período1/01/17 → …

Palabras clave

  • Data mining
  • Sequential patterns
  • Spatiotemporal data


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