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The idea of producing balanced-size clusters is to employ the techniques developed for the packing problem [10].
The main function of these clusters is to help their surrounding area to desolvate, to allow the hydrogen bond network, therefore favoring the formation of a stable helix.
The purpose of expanding the clusters is to increase the embedding capacity, and the reason of selecting the virtual colours near the centroids is to minimise the embedding distortion.
One possibility to describe such partially ordered clusters is to assume that the easy anisotropy axes of the nanoparticles are uniformly distributed in a solid angle, θ < θ max, in the spherical coordinates.
Then, the spectral clustering to divide the kth subshot into M clusters is to solve a relaxed optimization problem: begin{array}rcl@ underset{mathbf{Y}}{text{max}} ; text{trace}(mathbf{Y}^{T}mathbf{LY}) quad text{s.t.} quad mathbf{Y}^{T}mathbf{Y}=mathbf{I}_{M} end{array} (1).
Since GO relates functions in a hierarchical fashion, the next challenge for evaluating clusters is to deal with this hierarchy.
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If patient genotypes would cluster purely at random, then the same clusters are to be expected in the control samples.
Nevertheless, the purpose of these clusters was to visualize apparent patterns and potential clusters of genes within the data.
One hundred and forty six clusters are to be enrolled in the trial.
The aim of clustering is to collect data points.
In WSNs, clustering is to organize scattered sensor nodes into a cluster-topology network for communications.
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