Neural Networks (SOM) Applied to INAA Data of Chemical Elements in Archaeological Ceramics from Central Amazon

Abstract

ABSTRACT Artificial neural networks represent an alternative to traditional multivariate techniques, such as principal component and discriminant analysis, which rely on hypotheses regarding the normal distribution of the data and homoscedasticity. They also may be a powerful tool for multivariate modeling of systems that do not present linear correlation between variables, as well as to visualize high-dimensional data in bi- or trivariate structures. One special kind of neural network of interest in archaeometric studies is the Self-Organizing Map (SOM). SOMs can be distinguished from other neural networks for preserving the topological features of the original multivariate space. In this study, the self-organizing maps were applied to concentration data of chemical elements measured in archaeological ceramics from Central Amazon using instrumental neutron activation analysis (INAA). The main objective was testing the chemical patterns previously identified using cluster and principal component analysis, forming groups of ceramics according the multivariate chemical composition. It was verified by statistical tests that the chemical elemental data was not normally distributed and did not present homogeneity of covariance matrices for different groups, as requested by principal component analysis and other multivariate techniques. The maps obtained were consistent with the patterns identified by cluster and principal component analysis, forming two chemical groups of pottery shards for each archaeological site tested. Finally, it was verified the potential of SOMs for testing if failures in underlying hypotheses of traditional multivariate techniques might be critically influencing the results and subsequent archaeological interpretation of archaeometric data.