In this study, we analyse 27-dimensional mtDNA haplogroup distributions of 174 Eurasian, North-African and American populations, including numerous ancient data as well. The main contribution of this work was the description of the haplogroup distribution of recent and ancient populations as compounds of certain hypothetic ancient core populations immediately or indirectly determining the migration processes in Eurasia for a long time. To identify these core populations, we developed a new iterative algorithm determining clusters of the 27 mtDNA haplogroups studied having strong rank correlation among each other within a definite subset of the populations. Based on this study, the current Eurasian populations can be considered as compounds of three early core populations regarding to maternal lineages. We wanted to show that a simultaneous analysis of ancient and recent data using a new iterative rank correlation algorithm and the weighted SOC learning technique may reveal the most important and deterministic migration processes in the past. This technique allowed us to determine geographically, historically and linguistically well-interpretable clusters of our dataset having a very specific, hardly classifiable structure. The method was validated using a 2-dimensional stepping stone model.
Keywords: A new iterative algorithm; Data mining; Eurasian populations; Human demographic history; Rank correlation; mtDNA haplogroups.