Wahrscheinlichkeitstheorie Cluster analysis applied to regional geochemical data : Problems and possibilities

A large regional geochemical data set of O-horizon samples from a 188,000 km2 area in the European Arctic, analysed for 38 chemical elements, pH, electrical conductivity (both in a water extraction) and loss on ignition (LOI, 480 oC), was used to test the influence of different variants of cluster analysis on the results obtained. Due to the nature of regional geochemical data (neither normal nor log-normal, strongly skewed, often multi-modal data distributions), cluster analysis results usually strongly depend on the clustering algorithm selected. Deleting or adding just one element (variable) in the input matrix can also drastically change the results of cluster analysis. Different variants of cluster analysis can lead to surprisingly different results even when using exactly the same input data. Given that selection of elements is often based on availability of analytical packages (or detection limits) rather than on geochemical reasoning this is a disturbing result. Cluster analysis can be used to group samples and to develop ideas about the multivariate geochemistry of the data set at hand. It should not be misused as a statistical "proof" of certain relationships in the data. The use of cluster analysis as an exploratory data analysis tool requires a powerful program system, able to present the results in a number of easy to grasp graphics. In the context of this work, such a tool has been developed as a package for the R statistical software.

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