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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)

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.

Keywords

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

References

  1. 1.
    National Network of meteorological stations—Spanish Agency of Meteorology. http://www.aemet.es/es/eltiempo/observacion/ultimosdatos
  2. 2.
    Jain AK, Murty MN, Flynn PJ (1999) Data clustering: a review. ACM Comput Surv (CSUR) 31(3):264–323CrossRefGoogle Scholar
  3. 3.
    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 6:1–18CrossRefGoogle Scholar
  4. 4.
    Tian W, Zheng Y, Yang R, Ji S, Wang J (2015) A survey on clustering based meteorological data mining. Int J Grid Distributed Comput 7:229–240Google Scholar
  5. 5.
    Hotelling H (1933) Analysis of a complex of statistical variables into principal components. J Educ Psychol 24:417–444CrossRefGoogle Scholar
  6. 6.
    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):81–95CrossRefGoogle Scholar
  7. 7.
    Aparna K, Nair MK (2015) Comprehensive study and analysis of partitional data clustering techniques. Int J Bus Anal (IJBAN) 2:23–38CrossRefGoogle Scholar
  8. 8.
    Anil K (2010) J.: Data clustering: 50 years beyond K-means. Pattern Recogn Lett 31:651–666CrossRefGoogle Scholar
  9. 9.
    Barlow H (1989) Unsupervised learning. Neural Comput 1:295–311CrossRefGoogle Scholar
  10. 10.
    Jain AK, Maheswari S (2013) Survey of recent clustering techniques in data mining. J Curr Comput Sci Technol 3Google Scholar
  11. 11.
    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)Google Scholar
  12. 12.
    Kohonen T (1990) The self-organizing map. Proc IEEE 78:1464–1480CrossRefGoogle Scholar
  13. 13.
    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 13:41–46Google Scholar
  14. 14.
    Park HS, Jun CH (2009) A simple and fast algorithm for K-medoids clustering. Expert Syst Appl 36:3336–3341CrossRefGoogle Scholar
  15. 15.
    Day WHE, Edelsbrunner H (1984) Efficient algorithms for agglomerative hierarchical clustering methods. J Classif 1:7–24CrossRefzbMATHGoogle Scholar
  16. 16.
    Hotelling H (1933) Analysis of a complex of statistical variables into principal components. J Educ Psychol 24:498–520CrossRefGoogle Scholar
  17. 17.
  18. 18.
    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 16–27Google Scholar

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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