Model-based cluster analysis of artefact compositional data

Abstract

Cluster analysis is the most widely used multivariate technique in archaeometry, with the majority of applications being exploratory in nature. Model-based methods of clustering have their advocates, but have seen little application to archaeometric data. The paper investigates two such methods. They have potential advantages over exploratory techniques, if successful. Mixture maximum-likelihood worked well using low-dimensional lead isotope data, but had problems coping with higher-dimensional ceramic compositional data. For our most challenging example, classification maximum-likelihood performed comparably with more standard methods, but we find no evidence to suggest that it should supplant these.