Cluster analysis for the selection of potential discriminatory variables and the identification of subgroups in archaeometry

What is it about?

In this paper, we exposed some difficulties occurring in current applications of traditional multivariate methods for identifying subgroups in chemical data measured from archaeological materials. To overcome these difficulties, three variable selection methods, based on Gaussian mixture models, are proposed and applied to published archaeometric data. The results show that methods for the selection of variables increase the efficiency and accuracy of the classification compared to traditional methods.

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Why is it important?

In this paper, we exposed some difficulties occurring in current applications of traditional multivariate methods for identifying subgroups in chemical data measured from archaeological materials. To overcome these difficulties, three variable selection methods, based on Gaussian mixture models, are proposed and applied to published archaeometric data. The results show that methods for the selection of variables increase the efficiency and accuracy of the classification compared to traditional methods.