# Independent component analysis and clustering for pollution data

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## 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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