Abstract
In archaeometry the focus is mainly on chemical analysis of archaeological artifacts such as glass objects or pottery. Usually the artefacts are characterized by their chemical composition. Here the focus is on cluster analysis of compositional data. Using Euclidean distances cluster analysis is closely related to principal component analysis (PCA) that is a frequently used multivariate projection technique in archaeometry. Since PCA and cluster analysis based on Euclidean distances are scale dependent, some kind of “appropriate” data transformation is necessary. Some different techniques of data preparation will be presented. We consider the log-ratio transformation of Aitchison and the transformation into ranks in more detail. From the statistical point of view the latter is a robust method.
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Mucha, HJ., Bartel, HG., Dolata, J. (2008). Effects of Data Transformation on Cluster Analysis of Archaeometric Data. In: Preisach, C., Burkhardt, H., Schmidt-Thieme, L., Decker, R. (eds) Data Analysis, Machine Learning and Applications. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78246-9_80
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DOI: https://doi.org/10.1007/978-3-540-78246-9_80
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