Determining Provenance from Compositional Data
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Determining Provenance from Compositional Data
Traditionally, classical multivariate statistical methods have been applied to relate cultural materials recovered at archaeological sites to their respective raw material sources. This Element reconsiders the use of statistical methods for provenance analysis of archaeological materials using a step-by-step procedure.
This volume highlights the classification methods in chemometrics, artificial intelligence, machine learning and knowledge discovery.
Traditionally, classical multivariate statistical methods have been applied to relate cultural materials recovered at archaeological sites to their respective raw material sources. However, when reviewing published research, which usually claims to have reached a high degree of confidence in the assignment of materials, the authors have detected that those applying these methods can make serious errors that compromise the inferences made.
Determining Provenance from Compositional Data reconsiders the use of statistical methods to address the problem of provenance analysis of archaeological materials using a step-by-step procedure that allows the recognition of natural groups in the data. This approach results in better quality classifications while avoiding the problems of total or partial overlaps in the chemical groups, which are common in biplots.
To evaluate the methods proposed here, the challenge of group search in ceramic materials is addressed using algorithms derived from model-based clustering. For cases with partial data labelling, a semi-supervised algorithm is applied to obsidian samples.
Series: Elements in Current Archaeological Tools and Techniques
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INFORMATION
ISBN: 9781009634168
Publisher: Cambridge University Press
Format: Hardback
Date Published: 26 March 2026
Country: United Kingdom
Imprint: Cambridge University Press
Illustration: Worked examples or Exercises
Audience: General / adult
DIMENSIONS
Weight: 262g
Pages: 86
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