Using Gaussian Mixture Model Clustering to Explore Morphology and Standardized Production of Ceramic Vessels: A Case Study of Pottery from Late Bronze Age Greece

This paper examines vessel morphology and degrees of standardization of ceramic vessels using Gaussian mixture model cluster analysis (GMMC). GMMC is an unsupervised data mining technique that identifies natural groups in a dataset. This project first tests whether GMMC can classify certain shape categories correctly according to human assigned Furumark Shape (FS) pot types using basic vessel dimensions. We then propose that vessels can be considered standardized if they were grouped into the cluster of their own shape category (or pot type). Vessel data are derived from the Late Bronze Age Petsas House ceramic workshop, located in the settlement of Mycenae in southern Greece. The sample comes from a sealed well deposit found within the workshop. GMMC identified three clusters within a group of 488 pots that correspond to three known vessel types with a high degree of sensitivity and specificity so that clustered shapes mostly align with shape categories, and each shape can be defined as standardized. The maximization of information enabled by GMMC and the ability to analyze multiple interrelated variables can thus indicate cognitive approaches to vessel production, such as the perception of vessel shape by potters, and socio-economic factors relating to use by consumers.