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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.
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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.
So far we have assumed that the algorithm finds the correct cluster allocation every time (CI = 0).
We show that the expected time complexity to find the correct cluster allocation of the prototypes is polynomial.
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).
With our clustering benchmark data sets, we compare the results to the known ground truth and observe that random swap finds the correct cluster allocation every time.
Reaching CI = 0 by RKM seems unrealistic, indicating that it is not capable to solve the global cluster allocation in general.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com