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Environmental and Ecological Statistics

, Volume 22, Issue 1, pp 33–43 | Cite as

Independent component analysis and clustering for pollution data

  • Asis Kumar Chattopadhyay
  • Saptarshi Mondal
  • Atanu BiswasEmail author
Article

Abstract

Independent component analysis (ICA) is closely related to principal component analysis (PCA). Whereas ICA finds a set of source variables that are mutually independent, PCA finds a set of variables that are mutually uncorrelated. Here we consider an objective classification of different regions in central Iowa, USA, in order to study the pollution level. The study was part of the Soil Moisture Experiment 2002. Components responsible for significant variation have been obtained through both PCA and ICA, and the classification has been done by \(K\)-Means clustering. Result shows that the nature of clustering is significantly improved by the ICA.

Keywords

Circular data Distance Fast ICA algorithm Independent Component Analysis \(K\)-means clustering Negentropy Non-Gaussianity  Principal Component Analysis 

Notes

Acknowledgments

The authors wish to thank the Editor, Professor Ashis SenGupta and two reviewers for their careful reading and constructive suggestions which led some improvement over two earlier versions of the paper.

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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Asis Kumar Chattopadhyay
    • 1
  • Saptarshi Mondal
    • 1
  • Atanu Biswas
    • 2
    Email author
  1. 1.Calcutta UniversityKolkataIndia
  2. 2.Indian Statistical InstituteKolkataIndia

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