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AutoCluster organizes your matches into shared match clusters that likely represent branches of your family. Each of the colored cells represents an intersection between two of your matches, meaning, they both match you and each other. These cells in turn are grouped together both physically and by color to create a powerful visual chart of your shared matches clusters. * *
Each color represents one shared match cluster. Members of a cluster match you and most or all of the other cluster members. Everyone in a cluster will likely be on the same ancestral line, although the MRCA between any of the matches and between you and any match may vary. The generational level of the clusters may vary as well. One may be your paternal grandmother’s branch, another may be your paternal grandfather’s father’s branch.
You may see several gray cells that do not belong to any color-grouped cluster. They usually represent a shared match where one of the two cousins is too closely related to you to belong to just one cluster. Each of these cousins belongs to a color-grouped cluster, the gray cell indicates that one of them belongs in both clusters. Unfortunately, the underlying code does not support multiple cluster membership.
* * For more information on match clustering, see Bettinger, Blaine T. “Clustering Shared Matches,” The Genetic Genealogist, 3 January 2017.
AutoCluster first organizes your DNA matches into shared match clusters that likely represent branches of your family. Everyone in a cluster will likely be on the same ancestral line, although the MRCA between any of the matches and between you and any match may vary. The generational level of the clusters may vary as well. One may be your paternal grandmother’s branch, another may be your paternal grandfather’s father’s branch. By comparing the trees from the members of a certain cluster, we can identify ancestors that are common amongst those trees. First, we collect the surnames that are present in the trees and create a network using the similarity between surnames. Next, we perform a clustering on this network to identify clusters of similar surnames. A similar clustering is performed based on a network using the first names of members of each surname cluster. Our last clustering uses the birth and death years of members of a cluster to find similar persons. As a consequence, initially large clusters (based on the surnames) are divided up into smaller clusters using the first name and birth/death year clustering. A total of 131 trees (73 linked, 58 unlinked, 131 public trees) have been identified for 123 DNA matches. From these trees, a total number of 4856 tree persons has been retrieved. The tree linked to the tested person is named den Braber/Easterly and contains 158 tree persons. In addition, the tested person can be recognized as the green visualization in the reconstructed trees. Next, in addition to identifying the common ancestors, we aim to visualize the common ancestors and try reconstruct the genealogical tree. In most cases only parts of the trees can be reconstructed. But, with some manual efforts, most automatically generated trees can be combined into one or several larger trees. To improve the analysis of the trees, we use a color gradient to differentiate the different DNA matches. In addition, persons in the tree are highlighted when you hover over the edges if they appear in different trees. Here is an example of such an reconstructed tree. The green person is the person that was tested. In red the DNA matches and the yellow/brown persons are tree persons retrieved from an unlinked tree.