Analysis of meteorological conditions in Spain by means of clustering techniques

作者：Arroyo Angel^*; Herrero Alvaro; Tricio Veronica; Corchado Emilio

来源：Journal of Applied Logic, 2017, 24: 76-89.

DOI：10.1016/j.jal.2016.11.026

• k-means
• SOM k-means
• k-medoids
• Agglomerative hierarchical clustering
• Cluster based on Gaussian Mixture Models
• Clustering evaluation techniques
• Meteorology

A comprehensive analysis of clustering techniques is presented in this paper through their application to data on meteorological conditions. Six partitional and hierarchical clustering techniques (k-means, k-medoids, SOM k-means, Agglomerative Hierarchical Clustering, and Clustering based on Gaussian Mixture Models) with different distance criteria, together with some clustering evaluation measures (Calinski Harabasz, Davies Bouldin, Gap and Silhouette criterion clustering evaluation object), present various analyses of the main climatic zones in Spain. Real-life data sets, recorded by AEMET (Spanish Meteorological Agency) at four of its weather stations, are analyzed in order to characterize the actual weather conditions at each location. The clustering techniques process the data on some of the main daily meteorological variables collected at these stations over six years between 2004 and 2010.

• 出版日期2017-11