Abstract
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.
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References
National Network of meteorological stations—Spanish Agency of Meteorology. http://www.aemet.es/es/eltiempo/observacion/ultimosdatos
Jain AK, Murty MN, Flynn PJ (1999) Data clustering: a review. ACM Comput Surv (CSUR) 31(3):264–323
Lu Y, Ma T, Yin C, Xie X, Tian W, Zhong S (2015) Implementation of the fuzzy C-means clustering algorithm in meteorological data. Int J Database Theory Appl 6:1–18
Tian W, Zheng Y, Yang R, Ji S, Wang J (2015) A survey on clustering based meteorological data mining. Int J Grid Distributed Comput 7:229–240
Hotelling H (1933) Analysis of a complex of statistical variables into principal components. J Educ Psychol 24:417–444
Michie S, Richardson M, Johnston M, Abraham C, Francis J, Hardeman W, Eccles MP, Cane J, Wood CE (2013) The behavior change technique taxonomy (v1) of 93 Hierarchically clustered techniques: building an international consensus for the reporting of behavior change interventions. Ann Behav Med 46(1):81–95
Aparna K, Nair MK (2015) Comprehensive study and analysis of partitional data clustering techniques. Int J Bus Anal (IJBAN) 2:23–38
Anil K (2010) J.: Data clustering: 50 years beyond K-means. Pattern Recogn Lett 31:651–666
Barlow H (1989) Unsupervised learning. Neural Comput 1:295–311
Jain AK, Maheswari S (2013) Survey of recent clustering techniques in data mining. J Curr Comput Sci Technol 3
Ding C, He X (2004) K-means clustering via principal component analysis. In: Proceedings of the twenty-first international conference on Machine learning, vol 29 (2004)
Kohonen T (1990) The self-organizing map. Proc IEEE 78:1464–1480
Napoleon D, Pavalakodi S (2011) A New method for dimensionality reduction using K means clustering algorithm for high dimensional data set. Int J Comput Appl 13:41–46
Park HS, Jun CH (2009) A simple and fast algorithm for K-medoids clustering. Expert Syst Appl 36:3336–3341
Day WHE, Edelsbrunner H (1984) Efficient algorithms for agglomerative hierarchical clustering methods. J Classif 1:7–24
Hotelling H (1933) Analysis of a complex of statistical variables into principal components. J Educ Psychol 24:498–520
Mathworks. http://es.mathworks.com/products/matlab/?refresh=true (2015)
Vesanto J, Himberg J, Alhoniemi E, Parhankangas J (1999) Self-organizing map in Matlab: the SOM toolbox. In: Proceedings of the Matlab DSP Conference, vol 99, pp 16–27
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Arroyo, Á., Tricio, V., Corchado, E., Herrero, Á. (2015). A Comparison of Clustering Techniques for Meteorological Analysis. In: Herrero, Á., Sedano, J., Baruque, B., Quintián, H., Corchado, E. (eds) 10th International Conference on Soft Computing Models in Industrial and Environmental Applications. Advances in Intelligent Systems and Computing, vol 368. Springer, Cham. https://doi.org/10.1007/978-3-319-19719-7_11
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DOI: https://doi.org/10.1007/978-3-319-19719-7_11
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