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Conclusion is provided in Section 7. Our goal is to identify the non-stationarity feature clusters that represent discriminative characteristics of each group.
An illustrative example of the clustering algorithm is depicted in Fig. 1 where APs are grouped in two clusters that represent two physical locations.
Analysing these criteria for varying data sets and cluster numbers allows us to identify sets of clusters that represent well the accumulation points of the feature values.
STRUCTURE estimates individual admixture conditioning on Hardy-Weinberg and linkage equilibrium on each of the K = 3 clusters, that represent the parental populations.
Over 90% of the genes were found to vary significantly over developmental time, and 71% of the genes were placed into clusters that represent patterns of differential expression over time.
Using the PL70 threshold we obtained 20 134 disjoint clusters that represent protein families.
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At sub-watershed scale, the derived morphometric parameters from four perspectives (drainage network, watershed geometry, drainage texture, and relief characteristics) were further grouped into three clusters (that represented low, moderate, and high values) and considerable spatial variability was observed.
More than half of the unidentified organisms were isolated at least twice, forming clusters that represented 95 novel taxa.
Within the clusters that represented at least 0.01% of the genome, only one cluster of LINE sequences [ 33] and two clusters of DNA transposons were identified.
Finally, the emergent categories from the data were grouped into meaningful clusters that represented the major findings from the study [ 37].
Analysis revealed four case clusters that represented 86.7% of the dataset (13.3% were excluded, due to a missing value for one or more variables).
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com