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Output: cluster configuration.
In these algorithms, a learning rate is used as a fuzzy membership value of the current input vector in the output cluster.
Table 1 Definitions of symbols used in Algorithm 1 Symbol Definition (mathbb {C}) Input cluster set k Specified number of output clusters (mathbb {R}) Output cluster set (top_k_clusters(mathbb {C}, k)) Top-k clusters (in mathbb {C}) (inner_edges(c)) Inner edges of cluster c neighbors(c) Adjacent clusters of cluster c (cut_edges(n, m)) Cut edges between cluster n and m.
The relative abundance of each sample in a cluster was used to create an abundance matrix using the output cluster files from the CD-HIT program, the files containing the original fasta sequences and headers for each sample (abundanceMatrix-twoStep.pl).
The granularity of the output cluster was set with an inflation value of 2.5.
Accuracy was evaluated on the simulated datasets using the ARI calculated between the output cluster partitions and the true partitions.
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The final output clusters are stored in a result file.
The input is clusters (mathbb {C}), and the specified number of output clusters is k.
There are input, hidden, and output clusters, where each cluster contains one or more neurons.
We treat them as seed clusters and put them into output clusters (mathbb {R}) (line 1).
Its disadvantage is that the output clusters could not reflect the real structure of the mapping in the output space.
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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