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These clusters can then serve as building blocks for larger printable objects.
However, the left over compounds or clusters can then be exported as SD-file.
External criteria for evaluating obtained clusters can then be used for comparing performances of different community detection approaches.
These clusters can then be used to develop the process based conversion plant models which are representative of regional conditions [15].
The identified clusters can then form the basis for deriving a hypothesis for the development of a predictive model for new data of the same type.
The obtained annotation clusters can then be ranked according to the statistical significance of cluster enrichment.
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The distribution and clustering can then be quantified with point-pattern algorithms31 such as Ripley K-function32, pair correlation analysis33 and DBSCAN (density-based spatial clustering application with noise 34.
A cluster can then have variable size depending on the propagation conditions as shown in Figure 1 and how the users are distributed within each cell.
The cluster can then be used to execute resource-demanding and computationally-expensive tasks, such as launching large populations of agents.
The support for the existence of a particular cluster can then be presented as a p-value, or as a stability value calculated as the frequency of occurrence of that cluster across a range of R-values.
The definition of a primary cluster can then be applied to the thus reduced set of objects with its clustering pattern.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com