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Each clustering i was assigned a score S i.
There are two main advantages of the property-based clustering: (i) the property-based clustering allows researchers to directly control the number of clusters shown on the screen through a parameter K of its underlying algorithm.
Several were used to check the automated genotype calling and the sample clustering: (i) the Call Rate is the number of SNP successfully genotyped for each sample; (ii) the GenTrain Score evaluates the confidence of the genotyping for one SNP on all samples.
We then clustered all proteins using single linkage clustering; i.e. all connected vertices were assigned to the same cluster.
This leads to a reduction in statistical efficiency due to clustering, i.e. the design effect.
Finally, in the assessment of the bias in the estimates of the effect of risk factors for clustering, I assigned risk status randomly within groups of clustered and unique cases.
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Both two clusters, i.e., Ncl = 2, and three clusters, i.e., Ncl = 3, are considered in this example.
In this case we need to distinguish between two different categories of clusters: (i) Category i which has only one cluster, i.e. C i.
Additionally, applications modeling animal density often use clusters (i.e., groups of individuals) but omit differences in cluster sizes.
end{aligned} (13 where (n_i) denotes the size of the cluster i, (n^k_i) is the number of instances that are correctly assigned in the cluster i, and k denotes the dominant class in the cluster i.
where |A i | represents the number of data points belonging to cluster i.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com