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. 2019 May 28;20:266. doi: 10.1186/s12859-019-2889-z

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© The Author(s) 2019

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. 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.

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Fig. 4 — Dunn index, Silhouette width and Davies–Bouldin index for assessing the number of groups in k-means algorithm. The figure presents the evaluation of the number of groups by means of two metrics, which combine the measures of compactness and separation of the clusters. Briefly, the higher the value of both indexes, the better the division into clusters. The figure shows that for the data set examined in the presented work, the optimal number of groups is 4, which agrees with the figure of sensitivity and precision - for example, precision for CODEX tool