Abstract
In provenance analysis, identifying the origin of the archaeological artifacts plays a significant role. Usually, this problem is addressed by discovering natural groups in data measured with spectroscopic techniques. Then, principal component and classical partitioning cluster analysis are employed to reveal the groups that supposedly define the origin of the investigated artefacts. However, this work shows that maximizing the variance and searching for specific cluster structures can be misleading because it fails to discriminate clearly the different archeological sources. In contrast, the new methodology reveals several acknowledged geological sources present in the materials through the exploitation of emergence and swarm intelligence without prior assumptions about the data structures. A combination of unsupervised and semi-supervised machine learning and chemometric is applied on samples of Mesoamerican geological sources and obsidian artefacts collected from the archaeological site of Xalasco in Mexico. The analysis of the artifacts showed a preference of Xalasco inhabitants to local obsidian deposits. The results show that this approach, in terms of robustness, is suitable for handling unbiased quantitative spectral analysis of archaeological materials revealing the natural groups of archeological data.
Highlights
In recent years, the number of archaeometric investigations that make use of analytical techniques in the analysis of compositional data of various archaeological materials continues to grow considerably
The second matrix contained a total of n = 256 artefacts of unknown origin collected through systematic excavations in the archaeological site of Xalasco. These two matrices were combined in a single matrix with a total of n = 320 data points and p = 1752, where p corresponds to the spectral range of 38 to 1790 channel counts equivalent to the energy range of 0.76 to 35.8 keV of the detector resolution
Since the origin of the control samples was fully known, when combining this matrix with the matrix of the artefacts of unknown origin, we can determine whether the groups that we obtained reflect the actual structure of the groups present in the data
Summary
The number of archaeometric investigations that make use of analytical techniques in the analysis of compositional data of various archaeological materials continues to grow considerably. The specific approach is to identify groups of similar objects in terms of their chemical composition. One example of this type of studies is provenance analysis, which tries to relate archaeological materials to their original natural sources by discriminating their characteristic chemical fingerprint through the application of analytical and quantitative methods for compositional data. There is no consensus definition of what a ‘cluster’ is [9]. This is because the cluster depends largely on the context. When identifying homogeneous groups of objects [11], many clustering algorithms implicitly assume different structures of clusters [12]–[17]. It should be noted that some clustering algorithms could detect groups even if they do not have a clustering structure [16], [18]
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