A Comparative Analysis of Machine Learning Algorithms for Identifying Cultural and Technological Groups in Archaeological Datasets through Clustering Analysis of Homogeneous Data

Author:

Troiano Maurizio1ORCID,Nobile Eugenio2ORCID,Grignaffini Flavia1,Mangini Fabio1ORCID,Mastrogiuseppe Marco3,Conati Barbaro Cecilia4,Frezza Fabrizio1ORCID

Affiliation:

1. Department of Information Engineering, Electronics and Telecommunications (DIET), University of Rome “La Sapienza”, 00185 Rome, Italy

2. The Sonia and Marco Nadler Institute of Archaeology, Tel Aviv University, Tel Aviv 6997801, Israel

3. Department of Human Sciences, Link Campus University, 00165 Rome, Italy

4. Department of Sciences of Antiquities, University of Rome “La Sapienza”, 00185 Rome, Italy

Abstract

Machine learning algorithms have revolutionized data analysis by uncovering hidden patterns and structures. Clustering algorithms play a crucial role in organizing data into coherent groups. We focused on K-Means, hierarchical, and Self-Organizing Map (SOM) clustering algorithms for analyzing homogeneous datasets based on archaeological finds from the middle phase of Pre-Pottery B Neolithic in Southern Levant (10,500–9500 cal B.P.). We aimed to assess the repeatability of these algorithms in identifying patterns using quantitative and qualitative evaluation criteria. Thorough experimentation and statistical analysis revealed the pros and cons of each algorithm, enabling us to determine their appropriateness for various clustering scenarios and data types. Preliminary results showed that traditional K-Means may not capture datasets’ intricate relationships and uncertainties. The hierarchical technique provided a more probabilistic approach, and SOM excelled at maintaining high-dimensional data structures. Our research provides valuable insights into balancing repeatability and interpretability for algorithm selection and allows professionals to identify ideal clustering solutions.

Funder

National Centre for HPC Big Data & Quantum Computing

Publisher

MDPI AG

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