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Clustering is an unsupervised learning technique which assigns a set of objects (genes) into subsets (called clusters) so that the objects in the same clusters are similar according to some similarity metric [ 10, 11].
Certain kinds of molecular information, especially DNA sequences, can provide clearer support than morphological data ever could for species identification, particularly when species clusters are similar in appearance.
All the three clusters are similar to PC1 vs. PC2.
Where the more the shorter distance of joining implies the more clusters are similar.
This is a function of the variation only among cluster totals and so will be minimised if clusters are similar.
As shown in Fig. 7, the temporal orders of the VGP clusters are different for the two studies, but the positions of the clusters are similar.
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The clusters were similar in terms of the order of motivations, but differed in motivational level.
However, for the reduced data sets we get the global maxima for four clusters, while the quality for five clusters is similar to that for nine clusters.
The findings from the 10 clusters were similar (table 4).
The distribution of nodes among clusters was similar for both networks.
However, qualitatively, clusters were similar to those obtained with LOCF imputation.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com