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We first evaluate different parameter settings and hybridization schemes.
Since each model has arbitrary parameters, we tested a number of data sets using several different parameter settings.
For the different parameter settings, only small differences in the number of components retained are observed.
More than 40 complete sets of clusters, using different parameter settings, were generated and stored.
We used the average silhouette width (ASW) [ 36], to assess the quality of a given clustering and to compare the results of clusterings with different parameter settings.
Iterating through different parameter settings, we choose a k-mer of 75 bp as the best parameter set.
Slightly different parameter settings may direct to very different performance.
We used three different graph cluster algorithms (markov clustering, transitivity clustering, and affinity propagation) with in every case three different parameter settings representing different levels of cluster compactness.
Nevertheless, different problems may require different parameter settings for network architectures.
Performance (AUROC) with different parameter settings.
We tested six different parameter settings.
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