Abstract
Grouping techniques employ similarities within data to create new entities, which lend themselves to the interpretation process. This article presents three different grouping approaches, each originally developed independently, and applied to a common dataset of archaeological finds. The aim is not to search for the right approach or results, in a competing way, but rather to present the methods as complementary. It is also our intention to stress that a tight connection between theory and statistical modelling is indispensable. Indeed, the use of a particular methodology must be supported by an adapted theory; similarly, a theory without a proper methodological realisation will often not have any actual utility. The integration of theory and method is exemplified in the three case methods. The first method uses metal objects as cultural indicators. The study area is divided into a set of identical geographical units, characterized according to the type and proportions of indicators and grouped using hierarchical clustering. The second approach deals with cultures as standardisations between individuals, using ‘Typenspektrum’ as significant data for identifying different cultures. Groups are defined through kernel density estimation and a cluster analysis, followed by internal and external validation techniques. A third method characterizes the funerary ritual and grave-goods, using a similarity algorithm coupled with clustering procedures to compare the graves with one another. The outcome is validated with exploratory methods and compared to patterns from different contexts. The complementarity of the results shows that each approach sheds light on a certain facet of the same whole.
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Notes
The database contains approximately 80,000 finds from circa 12,000 sites dating from Hallstatt C to La Tène B. Twelve thousand nine hundred eighty finds from 215 sites, dating to Hallstatt D, are used in our analyses.
The methods were developed as part of two Ph.D. theses and a post-doctoral dissertation (habilitation) (Nakoinz 2013; Popa forthcoming; Tremblay Cormier 2016).
The excavation procedure and ideas from our present society also have an influence on the archaeological record.
Developed by L. Tremblay Cormier
Voronoi polygons are a space division method, where each polygon corresponds to the catchment area of a point. When using Euclidean metric, the polygon’s limits are set to the middle of the distance between its midpoint and the nearest neighbouring points.
Developed by O. Nakoinz
Figure 2 visualises this effect and is discussed below.
Developed by C. Popa
All the types cited in this article come from the typology of the Heuneburg’s fibulae (Mansfeld 1973, 4–5).
This apparent lack of regional differentiation may also be connected to poor data recording, since in analyses carried out on other datasets, clear regional differences were observable (Popa, 2012, 2014, forthcoming).
There are a countless number of studies on this topic; for the last synthesis, see Krauße and Beilharz (Ed.) 2010.
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Acknowledgements
Oliver Nakoinz is grateful to the Deutsche Forschungsgemeinschaft (DFG) for supporting this work with a Heisenberg fellowship (NA 687/1-1, NA 687/1-2) and to Dirk Krauße, Johannes Müller und Ulrich Müller for the opportunity to participate in the project ‘Siedlungshierarchien und kulturelle Räume’ in the DFG priority project 1171.
Cătălin Popa expresses his gratitude to the Dahlem Research School and the Excellence Cluster TOPOI for housing and funding his research.
Laurie Tremblay Cormier would like to thank the Université de Bourgogne and the Ministère de la Culture for hosting and funding her Ph.D. research.
Funding
The original analysis of approach two was funded by DFG (grand number KR 1753/2-3) while the integration of this approach into this study was funded by a DFG Heisenberg fellowship (grant number NA 687/1-1, NA 687/1-2).
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Tremblay Cormier, L., Nakoinz, O. & Popa, C.N. Three Methods for Detecting Past Groupings: Cultural Space and Group Identity. J Archaeol Method Theory 25, 643–661 (2018). https://doi.org/10.1007/s10816-017-9350-2
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DOI: https://doi.org/10.1007/s10816-017-9350-2