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Clusters were built using a similarity threshold of 0.7 with a complete linkage algorithm.
For the hierarchical methods the ward linkage algorithm performs best under our simulation design.
We also compare the proposed technique with three function-level clustering techniques Single Linkage algorithm (SLINK), Complete Linkage algorithm (CLINK) and Weighted Pair-Group Method using Arithmetic averages (WPGMA).
Here, we use a simple elimination method to remove extra solutions from the archive instead of single linkage algorithm and avoid its complexity.
Hierarchical clustering was performed using a centroid linkage algorithm with an uncentered Pearson correlation similarity metric.
The samples were clustered using a pairwise average linkage algorithm based on Spearman's rank correlation as a distance measure [47].
Both FastGroup and ESPRIT differ from the more controlled calculations of the complete linkage algorithm, but at the same time promise less computationally intensive results.
Hierarchical clustering of samples based on their predicted probability values of oncogenic pathway activation was performed using the complete linkage algorithm with the Euclidean distance metric [37].
For HC and the heatmap in Figure 3 the R function "hclust" was used in combination with the "heatmap.2" function using the "average linkage" algorithm.
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Clustering was done by means of a standard single-linkage algorithm with a Euclidean metric (Matlab, Mathworks).
The data were then log10 transformed and clustered using Genesis, using an average-linkage algorithm and a Euclidean distance metric [20].
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