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.
Document type :
Conference papers
Complete list of metadatas

Cited literature [18 references]  Display  Hide  Download

Contributor : Hugo Alatrista-Salas <>
Submitted on : Wednesday, December 3, 2014 - 11:23:02 PM
Last modification on : Wednesday, November 20, 2019 - 7:10:41 AM
Long-term archiving on : Saturday, April 15, 2017 - 2:52:43 AM


Files produced by the author(s)


  • HAL Id : lirmm-01090664, version 1


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. ⟨lirmm-01090664⟩



Record views


Files downloads