Partitioning Archaeological Chaînes Opératoires on R: dealing with huge categorical datasets?
Channel: Recording Archaeology
Date: 2019-10-09

A chaîne opératoire is an operational sequence describing the manufacturing process by which raw material is transformed into an artefact. It includes several main actions (variables) for which several possibilities exist (values). For example, one can fashion a pottery by molding it, and finish the vessel by smoothing the wall. Theories in anthropology of techniques state that one can link technical traditions to potters’ social memberships because they learn the craft in their own social group (cast, family, ethnic group...). Technical variants exist and can be related to ceramic functions. Settlements often reveal vast technical diversities. These schemas involve analysing a huge amount of categorical variables. Archaeological datasets are incomplete because of artefact preservation. Suitable mining and modeling tools are few. The function PACO for R attempts to solve these methodological problems. The guiding principle is based on Roux’s handbook for ceramic technology (2016). It is an iterative partitioning process which ranks technical variables according to their standardisation. PACO organises data, reveals norms and variants, avoids gaps, clusters similar chaînes opératoires. A classification tree (Breiman et al. 1984) then explains technical groups with external variables. This supervised classification algorithm finds rules guiding potters’ behaviours, depending on spatial and chronological contexts, but also on aesthetics and functional features of the containers. The issues of this procedure are explained with Late Bronze Age ceramics from the Upper Rhine valley, but in the end it concerns every technological dataset, whatever the size, materials and cultural frame.

Full Title: Partitioning Archaeological Chaînes Opératoires (PACO) on R : how to deal with huge incomplete categorical datasets?

(Marie Philippe)