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The SOM analysis distinguished two main clusters of samples, X and Y.
There were no clusters of samples according to PC2 (Fig. 1a).
PC1 revealed two clusters of samples, which were not explained by gender, sport types, or classes (Fig. 1a).
Then, the K-means clustering algorithm was used to generate clusters of samples belonging to new classes and eliminate the unrepresentative samples from each class.
Yang et al. defined interclass clusters of samples and found the optimal kernel combinations for each cluster in an image classification task [26].
Furthermore, a t test comparing the hemolysis measurement, between the two clusters of samples revealed by PC1, was significant at the 0.01 significance level.
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Open image in new window Fig. 12 Dendrogram showing the hierarchical clusters of sampling site.
These clusters of sampling points during dry season were grouping based on similar water quality characteristics.
It should be noted that clusters of sampling points were based on similar topography setting, characteristics location of the sampling sites and vicinity with respect to the dumpsite.
Unsupervised hierarchical clustering showed no clustering of samples based on endometriosis stages.
The two principal component model showed a clustering of samples, with a good reproducibility of the center points.
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