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Occasionally, cluster allocation occurs in several stages: the DRC survey first allocated clusters among different health zones, then distributed each zone's allotment of clusters among its villages.
Fig. 1 Cluster allocation by governorate.
Cluster allocation is optimized with two different approaches for comparison: using a mincut algorithm and using a genetic algorithm.
We show that the expected time complexity to find the correct cluster allocation of the prototypes is polynomial.
So far we have assumed that the algorithm finds the correct cluster allocation every time (CI = 0).
The clustering can therefore end-up with significantly different cluster allocation (10% different clusters) despite having virtually the same SSE-value (see "Optimality of the random swap" section).
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The review includes studies based on both individual and cluster allocations and is not restricted to RCTs.
The proportion of runs resulting in 95% of the coefficients being equal is used to assess the stability of the cluster allocations.
Further, using the results of the Ward algorithm as a starting point, a K-means cluster analysis was used to optimize cluster allocations and, if necessary, to reallocate the subjects to other clusters.
By deducing the emm-cluster allocation from the emm-typing results [ 1], we observed that the 56 US emm types belonged to 18 emm clusters (Table 1), whereas the 33 Canadian emm types belonged to 14 emm clusters (data not shown).
That there was perfect reproducibility of results (number of clusters, allocation of individuals to clusters, classification probability) is reassuring.
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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