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. 2020 Dec 16;21(Suppl 5):582. doi: 10.1186/s12864-020-06982-4

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© The Author(s) 2020

Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

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Fig. 5 — Example of overlap-based cluster merging. a Output of threshold-based hierarchical clustering, where circles represent hosts (k-mer distributions), that are connected with an edge if distance (EMD) between them doesn’t exceed a threshold (so that no unrelated hosts are connected). There are 2 clusters that belong to the same outbreak, which means that some related hosts are treated as unrelated. b For each cluster, circles were build, so that mean hosts reside in the center of the circle, and radius is defined as the distance between mean host and furthest host in an outbreak. Circle of cluster 1 intersects with cluster 2 since host X is closer to Mean 1 than furthest host in cluster 1. Therefore, clusters 1 and 2 are merged