Applying data mining methods for the analysis of stable isotope data in bioarchaeology

Data science methods have the potential to benefit other scientific fields by shedding new light on common questions. One such task is choosing good features for analysis. In this paper, we introduce a data science framework that was designed to allow domain experts to consider their domain knowledge in assembling suitable data sources for complex analyses. The structure of experimental data as represented by a clustering is used to measure the relevance as well as the redundancy of each feature. We present an application of this technique to bioarchaelogical data from a region in the European Alps, a transalpine passage of eminent archaeological importance in European prehistory, the Inn-Eisack-Adige passage, spanning Italy, Austria, and Germany. These results are applied to the task of provenance analysis. The application of the presented data mining technique leads to new insights which were not found using standard bioarchaeological approaches.