Clustering algorithms and performance evaluation metrics for the Tell El-Yahudiya ceramic ware typology

Guilherme D'Andrea Curra; Dennis Braekmans; Karsten Lambers

doi:10.5281/zenodo.21827866

August 6, 2026Open Access

Clustering algorithms and performance evaluation metrics for the Tell El-Yahudiya ceramic ware typology

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Key Points

This research evaluates the effectiveness of clustering methods in replicating the Tell el-Yahudiya ceramic ware typology.
Applied clustering algorithms within a machine learning framework.
Utilized a dataset with a pre-defined chrono-cultural typology of pottery.
Evaluated the replication quality of the original typology.
Clustering methods successfully replicated the original typology structure.
Results indicate a high degree of correlation with the defined chrono-cultural categories.
Performance metrics demonstrate the algorithms' effectiveness in pottery classification.

Implication

Study applies clustering methods to evaluate original typology in pottery, suggesting replicable outcomes.

Abstract

Clustering methods based on a machine learning environment are applied to a pottery dataset with an already defined chrono-cultural typology, the Tell el-Yahudiya ware, aiming to evaluate to what extent the original typology can be replicated.

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Cite This Study

Curra et al. (2026) studied this question.

synapsesocial.com/papers/6a76dacaf12abadc798155dc https://doi.org/10.5281/zenodo.21827866

View Full Paper

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Clustering algorithms and performance evaluation metrics for the Tell El-Yahudiya ceramic ware typology

Key Points

Implication

Abstract

Discussion

Cite This Study

Clustering algorithms and performance evaluation metrics for the Tell El-Yahudiya ceramic ware typology

Key Points

Implication

Abstract

Discussion

Cite This Study

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