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Formation flight of spacecraft clusters needs of reliable and accurate techniques to design the proximity maneuvers required in situations like the deployment or the reconfiguration of the formation.
Besides the lower performance of k-means, it has a disadvantage that the number of clusters needs to be specified beforehand.
In particular, we adopt a Dirichlet process mixture model with the advantages, with respect to the usual clustering methods, that the number of clusters needs not to be known and fixed in advance and the variation in the assignment of SNPs to clusters can be accounted.
The mechanism of action of stem cell-conditioned medium on islet-like cell clusters needs to clarify in the future.
Functional assessment of the RF-ve polyarticular and oligoarticular clusters needs to be determined.
Concerning k-Means and the Spectral clustering, the number of clusters needs to be defined by the user.
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That said, not all clusters need exhibit the flexibility and vagueness of these biological, homeostatic cases.
Only those involving references from the merged clusters need to be recomputed.
Moreover, the groupings of the clusters need more variation in response to locality.
Constrained clustering is another method of semi-supervised clustering within which the final clusters need to satisfy certain constrains.
Thus, only samples inside these clusters need to be optimized (see box 'Get ambiguous clusters from ' in Figure 2).
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com