Data mining for isotopic mapping of bioarchaeological finds in a central european alpine passage

Isotopic mapping has become an indispensable tool for the assessment of mobility and trade of the past. However, modeling and understanding spatio-temporal isotopic variation is complicated by the small number of available samples, potential mobility of the investigated samples, sample preservation quality, uncertainty of measurements, and so forth. In this work, we use data mining techniques to build an isotopic map (descriptive modeling) and to determine the spatial origin of new samples (predictive modeling). In particular, we propose a clustering-based isotope ratio model and a scoring function for the origin prediction of new samples. Our data was extracted from real animal finds from an Alpine passage that spans three countries (Germany, Austria, and Italy) and comprises a high variety of isotopes and geological characteristics. Our results and evaluation by domain experts show that it is possible to derive a model of the area for both descriptive and predictive purposes.