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Fuzzy clustering concept is based on general clustering.
In general, clustering problems are formalized as the maximization or minimization of the sum of metrics within each cluster.
Out of nine PET segmentation methods studied, clustering means (KM2), general clustering means (GCM3), adaptive thresholding (AT) and watershed thresholding (WT) methods were included for analysis.
General clustering techniques such as k-means seem to solve, at least in large part, this drawback, with (M_{k}) method obtaining results that are competitive with ad hoc expertise clustering approaches that are problem-dependent, as the one employed in (M_{expert}).
Overall, the plot shows a general clustering of AAs with similar properties with the first PC corresponding to hydrophobicity (F and I score high whereas D and E score low) and the second PC corresponding to size (W and K score high whereas G and A score low).
In general, clustering the 19 different variables showed that phenotypic markers, defined by flow cytometry, allow to segregate healthy individuals from individuals with neurological diseases.
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Three general clusters were generated based on informative SNPs that grouped by SGA levels for most of the accessions.
In general, cluster randomized controlled trials should be considered the golden standard for evaluation of ASPs.
The identification of subgroups via general cluster analysis is relatively common.
Onset in childhood is very rare, and about 5%to10%0% of cases in the general cluster headache population start in adolescence (mean age 11 14 years).
Finally, using general cluster formula [cluster](glue atoms)x(x = 1, 3), Mg-based Mg67Zn28Ca5 bulk metallic glass may be deciphered via the most close-packed eutectic cluster Zn2Mg11 as: [Zn2Mg11] + (Zn2Ca1).
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