A Comparison of Clustering Techniques for Meteorological Analysis

  • Ángel ArroyoEmail author
  • Verónica Tricio
  • Emilio Corchado
  • Álvaro Herrero
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 368)


Present work proposes the application of several clustering techniques (k-means, SOM k-means, k-medoids, and agglomerative hierarchical) to analyze the climatological conditions in different places. To do so, real-life data from data acquisition stations in Spain are analyzed, provided by AEMET (Spanish Meteorological Agency). Some of the main meteorological variables daily acquired by these stations are studied in order to analyse the variability of the environmental conditions in the selected places. Additionally, it is intended to characterize the stations according to their location, which could be applied for any other station. A comprehensive analysis of four different clustering techniques is performed, giving interesting results for a meteorological analysis.


Clustering techniques K-means SOM k-means K-medoids Agglomerative hierarchical clustering Meteorology 


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Ángel Arroyo
    • 1
    Email author
  • Verónica Tricio
    • 2
  • Emilio Corchado
    • 3
  • Álvaro Herrero
    • 1
  1. 1.Department of Civil EngineeringUniversity of BurgosBurgosSpain
  2. 2.Department of PhysicsUniversity of BurgosBurgosSpain
  3. 3.Departamento de Informática y AutomáticaUniversity of SalamancaSalamancaSpain

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