Archaeology, Ethnology & Anthropology of Eurasia 52 (3) 2024   doi:10.17746/1563-0110.2024.52.3.148-156

Annotation:  The Origin of H. s. denisovan and Their Dispersal Across Iran Methodological Issues in Pollen Analysis of Pleistocene Deposits in Denisova Cave Small Blade and Bladelet Production in Central Asia at the Turn of MIS 7 and 6: Cores from Kulbulak Identification of Adhesives for Repairing Ancient Ceramics: The Case of the Neolithic and Chalcolith Slag Inclusions in Iron Artifacts from Cemeteries at Kichigino I and Krasnaya Gorka, and the Metallu Radiocarbon Chronology and Isotope Data of Ust-Tartasskiye Kurgany Mound 51, the Baraba Forest-Stepp Raw Materials in the Paste of Ceramics of the Kulaika Culture Surgut Variant (Based on Samples from A Bronze Age Site on the Northern Coast of West Java Microstructural Study of Medieval Crucible Steels from Archaeological Sites in Central and Northwest Textiles from the Ust-Voikary Hillfort Site (Based on Materials from 2012–2016 Excavations) Shaman Tambourines of the Northern Ob Ugrians (18th to Early 21st Centuries) Principal Results of the Project Implemented by the Research and Educational Center for Altaic and T Bony Labyrinth in Upper Paleolithic Individuals Buried at Sungir The Structure of the Late Bronze Age Population of Western Siberia: Craniometric Evidence Affinities of the Sargat Population in the Baraba Forest-Steppe Application of the Decision Tree Method for Differentiating Human Groups   

Application of the Decision Tree Method for Differentiating Human Groups

O.A. Fedorchuk¹ and N.N. Goncharova^{1, 2}

¹Lomonosov Moscow State University, Leninskie Gory 1, bldg. 12, Moscow, 119234, Russia

²Bochkov Research Center for Medical Genetics, Moskvorechye 1, Moscow 115522, Russia

One of the tasks of modern biological anthropology is to develop a system that could objectively classify humanity on the basis of measurements. Here, the decision tree algorithm was chosen to create a classification of groups. The method helps to evaluate the differentiating power of specific dimensions for separating samples and to assess the composition of clusters at each step of the analysis. Standard cranial measurements were used, and the entropy index was chosen as a heterogeneity measure. Classification units were 39 ethno-territorial groups from 13 major regions of the Old World. At the first step, differentiation is made between broad-faced and narrow-faced groups, demonstrating the classificatory value of this trait. The first cluster includes only Mongoloids, admixed Southern Siberian populations, and Ainu. The second cluster is heterogeneous, but its further subdivision is more in line with the traditional classification. Traits underlying the branching of the tree may be the same in different branches, evidencing their taxonomic importance. Capabilities of the decision tree method proved sufficient to construct a system largely similar to the traditional one. Certain traits separate large groups of populations, while others are efficient at the regional level. The method, therefore, can be recommended as a supplementary tool at the intraspecific level.

Keywords: Biological anthropology, polymorphism, craniology, biostatistics, decision trees